Setup
Opening packages and data set
Sample Characteristics and centering/dummying
data <- data %>% mutate(across(c(htq1_04:htq1_39, ace_3_1:ace_7_3,ace_2_1R,ace_2_2R), ~ ifelse(. %in% c(88,99,100,89,777), NA, .))) %>% ungroup() %>% mutate_at(vars(c(htq1_04:htq1_39, ace_3_1:ace_7_3,ace_2_1R,ace_2_2R)),
funs(as.ordered(.)))
describe(data$q102b_guess_age)## data$q102b_guess_age
## n missing distinct Info Mean Gmd .05 .10
## 2965 0 37 0.995 22.3 6.351 15 16
## .25 .50 .75 .90 .95
## 18 21 26 30 33
##
## lowest : 12 13 14 15 16, highest: 44 46 47 48 66##
## Descriptive statistics by group
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2323 21.14 5.16 20 20.56 4.45 12 40 28 0.96 0.34 0.11
## ------------------------------------------------------------
## group: 2
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 642 26.48 6.23 25 25.75 5.93 16 66 50 1.29 2.76 0.25## Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing
## --------------------------------------------------------------------------------------------
## htq1_sum | 6.75 | 3.86 | 5.00 | [0.00, 27.00] | 0.73 | 0.63 | 2965 | 0
## ace_frequency | 4.39 | 2.04 | 3.00 | [0.00, 11.00] | 0.19 | -0.50 | 2959 | 6
## htq_ptsd_total | 1.89 | 0.53 | 0.76 | [1.00, 3.79] | 0.38 | -0.45 | 2804 | 161HTQ Run CFAs model with all sample
5 factor solution
model_5fw <- "
ConflictTrauma =~ htq1_05 + htq1_06 + htq1_17
Isolation_Loss =~ htq1_19 + htq1_21 + htq1_30_34
ViolentVictimization =~ htq1_7_10 + htq1_8_9 + htq1_11 + htq1_13_18 + htq1_14 + htq1_16 + htq1_20_24 + htq1_22 + htq1_25_to_29 + htq1_23
Destruction_Injury =~ htq1_04 + htq1_12_36 + htq1_15 + htq1_32 + htq1_35
WitnessViolence =~ htq1_39 + htq1_37 + htq1_38
htq1_04 ~~ htq1_15
"
fit_mod5fw <- lavaan::cfa(model_5fw, data = data, auto.fix.first = FALSE, std.lv = TRUE, ordered = T)
t5 <- fitMeasures(fit_mod5fw, fit.measures = c("npar", "fmin", "chisq", "df", "pvalue", "srmr", "cfi.scaled", "tli.scaled", "rmsea.scaled", "srmr_mplus"))
knitr::kable(t5, caption = "Model fit statistics 5", digits = 3)| x | |
|---|---|
| npar | 59.00000000 |
| fmin | 0.14739757 |
| chisq | 868.46647399 |
| df | 241.00000000 |
| pvalue | 0.00000000 |
| srmr | 0.07618437 |
| cfi.scaled | 0.96502237 |
| tli.scaled | 0.95994263 |
| rmsea.scaled | 0.03256266 |
| srmr_mplus | NA |
## lavaan 0.6.17 ended normally after 21 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 59
##
## Used Total
## Number of observations 2946 2965
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 868.466 993.561
## Degrees of freedom 241 241
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.917
## Shift parameter 46.308
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 34668.630 21791.509
## Degrees of freedom 276 276
## P-value 0.000 0.000
## Scaling correction factor 1.599
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.982 0.965
## Tucker-Lewis Index (TLI) 0.979 0.960
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.030 0.033
## 90 Percent confidence interval - lower 0.028 0.030
## 90 Percent confidence interval - upper 0.032 0.035
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076 0.076
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma =~
## htq1_05 0.748 0.022 34.273 0.000 0.748 0.748
## htq1_06 0.904 0.025 36.610 0.000 0.904 0.904
## htq1_17 0.862 0.020 43.686 0.000 0.862 0.862
## Isolation_Loss =~
## htq1_19 0.893 0.023 38.842 0.000 0.893 0.893
## htq1_21 0.937 0.023 39.922 0.000 0.937 0.937
## htq1_30_34 0.443 0.054 8.273 0.000 0.443 0.443
## ViolentVictimization =~
## htq1_7_10 0.637 0.029 22.322 0.000 0.637 0.637
## htq1_8_9 0.562 0.040 14.176 0.000 0.562 0.562
## htq1_11 0.780 0.020 38.169 0.000 0.780 0.780
## htq1_13_18 0.685 0.034 20.317 0.000 0.685 0.685
## htq1_14 0.851 0.019 45.932 0.000 0.851 0.851
## htq1_16 0.555 0.027 20.634 0.000 0.555 0.555
## htq1_20_24 0.685 0.039 17.463 0.000 0.685 0.685
## htq1_22 0.683 0.030 22.551 0.000 0.683 0.683
## htq1_25_to_29 0.402 0.044 9.197 0.000 0.402 0.402
## htq1_23 0.624 0.045 13.894 0.000 0.624 0.624
## Destruction_Injury =~
## htq1_04 0.642 0.024 26.635 0.000 0.642 0.642
## htq1_12_36 0.685 0.024 28.545 0.000 0.685 0.685
## htq1_15 0.693 0.022 31.576 0.000 0.693 0.693
## htq1_32 0.614 0.023 26.508 0.000 0.614 0.614
## htq1_35 0.664 0.024 27.396 0.000 0.664 0.664
## WitnessViolence =~
## htq1_39 0.690 0.019 36.298 0.000 0.690 0.690
## htq1_37 0.836 0.014 59.196 0.000 0.836 0.836
## htq1_38 0.933 0.012 74.872 0.000 0.933 0.933
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq1_04 ~~
## .htq1_15 0.316 0.027 11.857 0.000 0.316 0.571
## ConflictTrauma ~~
## Isolation_Loss 0.428 0.038 11.305 0.000 0.428 0.428
## ViolentVctmztn 0.325 0.032 10.157 0.000 0.325 0.325
## Destrctn_Injry 0.707 0.026 26.912 0.000 0.707 0.707
## WitnessViolenc 0.520 0.025 20.419 0.000 0.520 0.520
## Isolation_Loss ~~
## ViolentVctmztn 0.509 0.032 15.785 0.000 0.509 0.509
## Destrctn_Injry 0.594 0.031 19.162 0.000 0.594 0.594
## WitnessViolenc 0.544 0.028 19.156 0.000 0.544 0.544
## ViolentVictimization ~~
## Destrctn_Injry 0.681 0.026 26.627 0.000 0.681 0.681
## WitnessViolenc 0.763 0.019 40.105 0.000 0.763 0.763
## Destruction_Injury ~~
## WitnessViolenc 0.728 0.021 34.694 0.000 0.728 0.728
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## htq1_05|t1 -0.692 0.025 -27.444 0.000 -0.692 -0.692
## htq1_06|t1 -1.331 0.032 -41.194 0.000 -1.331 -1.331
## htq1_17|t1 -0.876 0.027 -32.913 0.000 -0.876 -0.876
## htq1_19|t1 1.131 0.029 38.534 0.000 1.131 1.131
## htq1_21|t1 1.193 0.030 39.526 0.000 1.193 1.193
## htq1_30_34|t1 1.656 0.039 42.211 0.000 1.656 1.656
## htq1_7_10|t1 1.296 0.032 40.849 0.000 1.296 1.296
## htq1_8_9|t1 1.716 0.041 41.974 0.000 1.716 1.716
## htq1_11|t1 0.982 0.028 35.546 0.000 0.982 0.982
## htq1_13_18|t1 1.620 0.038 42.293 0.000 1.620 1.620
## htq1_14|t1 1.118 0.029 38.310 0.000 1.118 1.118
## htq1_16|t1 0.963 0.027 35.101 0.000 0.963 0.963
## htq1_20_24|t1 1.835 0.045 41.148 0.000 1.835 1.835
## htq1_22|t1 1.411 0.034 41.823 0.000 1.411 1.411
## htq1_25_t_29|1 1.623 0.038 42.287 0.000 1.623 1.623
## htq1_23|t1 1.883 0.046 40.692 0.000 1.883 1.883
## htq1_04|t1 -0.536 0.024 -22.018 0.000 -0.536 -0.536
## htq1_12_36|t1 0.989 0.028 35.703 0.000 0.989 0.989
## htq1_15|t1 -0.020 0.023 -0.884 0.377 -0.020 -0.020
## htq1_32|t1 0.664 0.025 26.526 0.000 0.664 0.664
## htq1_35|t1 0.890 0.027 33.278 0.000 0.890 0.890
## htq1_39|t1 0.435 0.024 18.211 0.000 0.435 0.435
## htq1_37|t1 -0.035 0.023 -1.510 0.131 -0.035 -0.035
## htq1_38|t1 0.161 0.023 6.923 0.000 0.161 0.161
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq1_05 0.441 0.441 0.441
## .htq1_06 0.183 0.183 0.183
## .htq1_17 0.257 0.257 0.257
## .htq1_19 0.202 0.202 0.202
## .htq1_21 0.122 0.122 0.122
## .htq1_30_34 0.804 0.804 0.804
## .htq1_7_10 0.594 0.594 0.594
## .htq1_8_9 0.684 0.684 0.684
## .htq1_11 0.391 0.391 0.391
## .htq1_13_18 0.531 0.531 0.531
## .htq1_14 0.275 0.275 0.275
## .htq1_16 0.692 0.692 0.692
## .htq1_20_24 0.531 0.531 0.531
## .htq1_22 0.533 0.533 0.533
## .htq1_25_to_29 0.839 0.839 0.839
## .htq1_23 0.611 0.611 0.611
## .htq1_04 0.588 0.588 0.588
## .htq1_12_36 0.530 0.530 0.530
## .htq1_15 0.519 0.519 0.519
## .htq1_32 0.622 0.622 0.622
## .htq1_35 0.559 0.559 0.559
## .htq1_39 0.524 0.524 0.524
## .htq1_37 0.301 0.301 0.301
## .htq1_38 0.130 0.130 0.130
## ConflictTrauma 1.000 1.000 1.000
## Isolation_Loss 1.000 1.000 1.000
## ViolentVctmztn 1.000 1.000 1.000
## Destrctn_Injry 1.000 1.000 1.000
## WitnessViolenc 1.000 1.000 1.000idx <- lavInspect(fit_mod5fw, "case.idx")
fscores <- lavPredict(fit_mod5fw, transform = T)
## loop over factors
for (fs in colnames(fscores)) {
data[idx, fs] <- fscores[ , fs]
}
head(data)## hhid_int restype respondent_cat q102b_guess_age primiparous htq1_04 htq1_05
## 1 3 1 0 -8.2957841 1 0 0
## 2 4 1 0 -4.2957841 1 0 1
## 3 6 1 0 -0.2957841 0 1 1
## 4 7 1 0 2.7042159 0 1 1
## 5 8 1 0 -7.2957841 1 0 1
## 6 9 1 0 4.7042159 0 1 1
## htq1_06 htq1_7_10 htq1_8_9 htq1_11 htq1_12_36 htq1_13_18 htq1_14 htq1_15
## 1 0 0 0 0 0 0 0 0
## 2 1 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0 0
## 4 1 0 0 0 0 0 0 1
## 5 1 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 0 1
## htq1_16 htq1_17 htq1_19 htq1_20_24 htq1_21 htq1_22 htq1_23 htq1_25_to_29
## 1 0 0 0 0 0 0 0 0
## 2 0 1 0 0 0 0 0 0
## 3 0 1 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 1 0 0 0 0 0 1
## htq1_30_34 htq1_32 htq1_35 htq1_37 htq1_38 htq1_39 htq1_sum htq_ptsd_dsm
## 1 0 0 0 1 0 0 1 2.8750
## 2 0 0 0 0 1 1 5 2.5000
## 3 0 0 0 0 0 0 4 1.3125
## 4 0 0 0 0 0 0 5 2.6875
## 5 0 0 0 1 0 0 3 1.5000
## 6 0 0 0 1 1 0 8 2.3750
## htq_ptsd_total ace_2_1 ace_2_2 ace_3_1 ace_3_2 ace_3_3 ace_4_1 ace_4_2
## 1 2.666667 3 3 0 0 3 0 0
## 2 2.212121 4 4 0 0 2 0 1
## 3 1.272727 3 1 0 0 3 0 0
## 4 2.272727 4 4 0 0 0 0 0
## 5 1.363636 4 4 0 0 0 0 0
## 6 2.696970 2 4 0 0 0 0 0
## ace_4_3 ace_4_4 ace_4_5 ace_4_6 ace_4_7 ace_4_8 ace_5_1 ace_5_2 ace_5_3
## 1 0 0 0 2 0 0 2 0 3
## 2 1 0 0 0 0 0 2 2 3
## 3 1 1 1 0 0 0 2 0 2
## 4 0 0 0 2 2 0 0 0 2
## 5 0 0 0 2 1 0 3 0 2
## 6 0 0 0 3 3 3 3 0 3
## ace_5_4 ace_5_5 ace_5_6 ace_5_7 ace_5_8 ace_6_1 ace_6_2 ace_6_3 ace_7_1
## 1 2 0 0 0 0 0 <NA> 0 3
## 2 0 0 0 0 0 3 3 3 3
## 3 2 0 0 0 0 0 <NA> 0 3
## 4 0 0 0 0 0 3 3 2 3
## 5 0 0 0 0 0 0 <NA> 2 3
## 6 3 0 0 0 0 0 <NA> 3 3
## ace_7_2 ace_7_3 ace_e_t ace_2_1R ace_2_2R ace_f_1 ace_f_2 ace_f_en
## 1 3 3 9:10:48 AM 1 1 0 0 0
## 2 3 2 8:30:00 AM 0 0 0 0 0
## 3 3 3 7:30:00 AM 1 3 0 1 1
## 4 3 3 2023-03-19 13-41 0 0 0 0 0
## 5 3 3 7:00:58 AM 0 0 0 0 0
## 6 3 3 8:36:07 AM 2 0 1 0 1
## ace_f_pn_1 ace_f_pn_2 ace_f_pn_3 ace_f_pn ace_b_ah ace_b_dh ace_b_ih
## 1 0 0 1 1 0 0 0
## 2 0 0 0 0 0 1 1
## 3 0 0 1 1 0 0 1
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## ace_b_dd_1 ace_b_dd_2 ace_b_dd ace_f_hv_1 ace_f_hv_2 ace_f_hv_3 ace_f_hv
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 1 1 1 0 0 0 0
## 4 0 0 0 0 1 0 1
## 5 0 0 0 0 0 0 0
## 6 0 0 0 1 1 1 1
## ace_f_ea_1 ace_f_ea_2 ace_f_ea ace_f_pa_1 ace_f_pa_2 ace_f_pa ace_b_sa_1
## 1 0 0 0 1 0 1 0
## 2 0 0 0 1 0 1 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 1 0 1 0 0 0 0
## 6 1 0 1 1 1 1 0
## ace_b_sa_2 ace_b_sa_3 ace_b_sa_4 ace_b_sa ace_f_bu_1 ace_f_bu_2 ace_f_bu
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 1 1 1
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 1
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 1 1
## ace_f_cv_1 ace_f_cv_2 ace_f_cv_3 ace_f_cv ace_frequency ConflictTrauma
## 1 1 1 1 1 3 -2.3493940
## 2 1 1 0 1 5 0.2513647
## 3 1 1 1 1 5 0.2390247
## 4 1 1 1 1 3 0.4390420
## 5 1 1 1 1 2 -0.9548046
## 6 1 1 1 1 6 0.6314491
## Isolation_Loss ViolentVictimization Destruction_Injury WitnessViolence
## 1 -0.9307988 -0.6049161 -1.55697116 -0.2831504
## 2 -0.5007248 -0.5105563 -0.89249830 0.4582630
## 3 -0.7271977 -1.0211406 -0.67100446 -1.0430733
## 4 -0.4869772 -0.8023438 -0.01772711 -0.9085330
## 5 -0.7894892 -0.7051607 -1.26074699 -0.2554927
## 6 -0.1012761 0.1908505 0.34142527 0.6998006ACE Run CFAs model with all sample
3 factor solution
model_3f <- "
#f1A_neglect =~
ChildAbuse =~ ace_4_6 + ace_4_7 + ace_4_8 + ace_5_1 + ace_5_2 + ace_5_3 + ace_5_4 + ace_6_1 + ace_6_3
ChildNeglectSexual =~ ace_2_1R + ace_2_2R + ace_3_1 + ace_3_2 + ace_4_1 + ace_5_5 + ace_5_6 + ace_5_7 + ace_5_8
ChildComViolence =~ ace_7_1 + ace_7_2 + ace_7_3
ace_3_2 ~~ ace_4_1
ace_2_1R ~~ ace_2_2R
ace_4_6 ~~ ace_4_7
ace_4_7 ~~ ace_4_8
"
fit_mod3f <- lavaan::cfa(model_3f, data = data, auto.fix.first = FALSE, std.lv = TRUE)
t4 <- fitMeasures(fit_mod3f, fit.measures = c("npar", "fmin", "chisq", "df", "pvalue", "srmr", "cfi.scaled", "tli.scaled", "rmsea.scaled"))
knitr::kable(t4, caption = "Model fit statistics 4F", digits = 3)| x | |
|---|---|
| npar | 79.00000000 |
| fmin | 0.22526725 |
| chisq | 1304.74790559 |
| df | 182.00000000 |
| pvalue | 0.00000000 |
| srmr | 0.09205578 |
| cfi.scaled | 0.94764344 |
| tli.scaled | 0.93958858 |
| rmsea.scaled | 0.04442849 |
## lavaan 0.6.17 ended normally after 29 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 79
##
## Used Total
## Number of observations 2896 2965
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1304.748 1222.023
## Degrees of freedom 182 182
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.118
## Shift parameter 55.087
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 33282.714 20074.237
## Degrees of freedom 210 210
## P-value 0.000 0.000
## Scaling correction factor 1.665
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.966 0.948
## Tucker-Lewis Index (TLI) 0.961 0.940
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046 0.044
## 90 Percent confidence interval - lower 0.044 0.042
## 90 Percent confidence interval - upper 0.049 0.047
## P-value H_0: RMSEA <= 0.050 0.996 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.092 0.092
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ChildAbuse =~
## ace_4_6 0.679 0.014 48.802 0.000 0.679 0.679
## ace_4_7 0.663 0.016 40.314 0.000 0.663 0.663
## ace_4_8 0.750 0.016 47.617 0.000 0.750 0.750
## ace_5_1 0.736 0.013 54.849 0.000 0.736 0.736
## ace_5_2 0.613 0.021 29.427 0.000 0.613 0.613
## ace_5_3 0.633 0.015 42.021 0.000 0.633 0.633
## ace_5_4 0.677 0.016 41.725 0.000 0.677 0.677
## ace_6_1 0.552 0.019 29.704 0.000 0.552 0.552
## ace_6_3 0.554 0.017 31.979 0.000 0.554 0.554
## ChildNeglectSexual =~
## ace_2_1R 0.333 0.035 9.414 0.000 0.333 0.333
## ace_2_2R 0.264 0.034 7.702 0.000 0.264 0.264
## ace_3_1 0.612 0.051 11.987 0.000 0.612 0.612
## ace_3_2 0.621 0.077 8.082 0.000 0.621 0.621
## ace_4_1 0.498 0.075 6.619 0.000 0.498 0.498
## ace_5_5 0.844 0.036 23.727 0.000 0.844 0.844
## ace_5_6 0.837 0.044 18.846 0.000 0.837 0.837
## ace_5_7 0.456 0.047 9.657 0.000 0.456 0.456
## ace_5_8 0.574 0.061 9.346 0.000 0.574 0.574
## ChildComViolence =~
## ace_7_1 0.714 0.021 33.970 0.000 0.714 0.714
## ace_7_2 0.822 0.018 46.411 0.000 0.822 0.822
## ace_7_3 0.847 0.017 48.725 0.000 0.847 0.847
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ace_3_2 ~~
## .ace_4_1 0.594 0.076 7.797 0.000 0.594 0.873
## .ace_2_1R ~~
## .ace_2_2R 0.374 0.023 16.093 0.000 0.374 0.411
## .ace_4_6 ~~
## .ace_4_7 0.269 0.015 17.454 0.000 0.269 0.490
## .ace_4_7 ~~
## .ace_4_8 0.286 0.019 15.037 0.000 0.286 0.577
## ChildAbuse ~~
## ChildNeglctSxl 0.496 0.034 14.432 0.000 0.496 0.496
## ChildComViolnc 0.410 0.021 19.510 0.000 0.410 0.410
## ChildNeglectSexual ~~
## ChildComViolnc 0.054 0.038 1.427 0.154 0.054 0.054
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ace_4_6|t1 -0.465 0.024 -19.172 0.000 -0.465 -0.465
## ace_4_6|t2 -0.314 0.024 -13.244 0.000 -0.314 -0.314
## ace_4_6|t3 0.356 0.024 14.942 0.000 0.356 0.356
## ace_4_7|t1 -0.128 0.023 -5.461 0.000 -0.128 -0.128
## ace_4_7|t2 0.055 0.023 2.341 0.019 0.055 0.055
## ace_4_7|t3 0.597 0.025 24.013 0.000 0.597 0.597
## ace_4_8|t1 0.455 0.024 18.805 0.000 0.455 0.455
## ace_4_8|t2 0.638 0.025 25.417 0.000 0.638 0.638
## ace_4_8|t3 1.061 0.029 36.911 0.000 1.061 1.061
## ace_5_1|t1 -0.227 0.024 -9.653 0.000 -0.227 -0.227
## ace_5_1|t2 -0.108 0.023 -4.607 0.000 -0.108 -0.108
## ace_5_1|t3 0.376 0.024 15.717 0.000 0.376 0.376
## ace_5_2|t1 0.672 0.025 26.562 0.000 0.672 0.672
## ace_5_2|t2 0.838 0.027 31.599 0.000 0.838 0.838
## ace_5_2|t3 1.362 0.033 41.112 0.000 1.362 1.362
## ace_5_3|t1 -0.658 0.025 -26.097 0.000 -0.658 -0.658
## ace_5_3|t2 -0.503 0.024 -20.635 0.000 -0.503 -0.503
## ace_5_3|t3 0.236 0.024 10.024 0.000 0.236 0.236
## ace_5_4|t1 0.346 0.024 14.537 0.000 0.346 0.346
## ace_5_4|t2 0.518 0.024 21.182 0.000 0.518 0.518
## ace_5_4|t3 0.973 0.028 35.015 0.000 0.973 0.973
## ace_6_1|t1 -0.155 0.023 -6.612 0.000 -0.155 -0.155
## ace_6_1|t2 -0.104 0.023 -4.458 0.000 -0.104 -0.104
## ace_6_1|t3 0.258 0.024 10.950 0.000 0.258 0.258
## ace_6_3|t1 -0.281 0.024 -11.875 0.000 -0.281 -0.281
## ace_6_3|t2 -0.177 0.023 -7.577 0.000 -0.177 -0.177
## ace_6_3|t3 0.414 0.024 17.226 0.000 0.414 0.414
## ace_2_1R|t1 0.228 0.024 9.690 0.000 0.228 0.228
## ace_2_1R|t2 0.858 0.027 32.143 0.000 0.858 0.858
## ace_2_1R|t3 1.430 0.034 41.578 0.000 1.430 1.430
## ace_2_1R|t4 2.019 0.052 38.711 0.000 2.019 2.019
## ace_2_2R|t1 0.017 0.023 0.743 0.457 0.017 0.017
## ace_2_2R|t2 0.618 0.025 24.734 0.000 0.618 0.618
## ace_2_2R|t3 1.094 0.029 37.534 0.000 1.094 1.094
## ace_2_2R|t4 1.704 0.041 41.672 0.000 1.704 1.704
## ace_3_1|t1 1.563 0.037 41.966 0.000 1.563 1.563
## ace_3_2|t1 2.165 0.059 36.464 0.000 2.165 2.165
## ace_4_1|t1 2.019 0.052 38.711 0.000 2.019 2.019
## ace_5_5|t1 1.529 0.036 41.929 0.000 1.529 1.529
## ace_5_6|t1 1.956 0.050 39.521 0.000 1.956 1.956
## ace_5_7|t1 1.605 0.038 41.952 0.000 1.605 1.605
## ace_5_8|t1 2.203 0.061 35.818 0.000 2.203 2.203
## ace_7_1|t1 -2.032 0.053 -38.524 0.000 -2.032 -2.032
## ace_7_1|t2 -1.814 0.044 -40.977 0.000 -1.814 -1.814
## ace_7_1|t3 -0.808 0.026 -30.741 0.000 -0.808 -0.808
## ace_7_2|t1 -1.569 0.037 -41.968 0.000 -1.569 -1.569
## ace_7_2|t2 -1.263 0.031 -40.126 0.000 -1.263 -1.263
## ace_7_2|t3 -0.414 0.024 -17.226 0.000 -0.414 -0.414
## ace_7_3|t1 -0.985 0.028 -35.302 0.000 -0.985 -0.985
## ace_7_3|t2 -0.826 0.026 -31.257 0.000 -0.826 -0.826
## ace_7_3|t3 -0.132 0.023 -5.647 0.000 -0.132 -0.132
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ace_4_6 0.539 0.539 0.539
## .ace_4_7 0.561 0.561 0.561
## .ace_4_8 0.437 0.437 0.437
## .ace_5_1 0.459 0.459 0.459
## .ace_5_2 0.624 0.624 0.624
## .ace_5_3 0.600 0.600 0.600
## .ace_5_4 0.542 0.542 0.542
## .ace_6_1 0.695 0.695 0.695
## .ace_6_3 0.693 0.693 0.693
## .ace_2_1R 0.889 0.889 0.889
## .ace_2_2R 0.930 0.930 0.930
## .ace_3_1 0.625 0.625 0.625
## .ace_3_2 0.615 0.615 0.615
## .ace_4_1 0.752 0.752 0.752
## .ace_5_5 0.288 0.288 0.288
## .ace_5_6 0.299 0.299 0.299
## .ace_5_7 0.792 0.792 0.792
## .ace_5_8 0.671 0.671 0.671
## .ace_7_1 0.491 0.491 0.491
## .ace_7_2 0.325 0.325 0.325
## .ace_7_3 0.282 0.282 0.282
## ChildAbuse 1.000 1.000 1.000
## ChildNeglctSxl 1.000 1.000 1.000
## ChildComViolnc 1.000 1.000 1.000idx <- lavInspect(fit_mod3f, "case.idx")
fscores <- lavPredict(fit_mod3f, transform = T)
## loop over factors
for (fs in colnames(fscores)) {
data[idx, fs] <- fscores[ , fs]
}
head(data)## hhid_int restype respondent_cat q102b_guess_age primiparous htq1_04 htq1_05
## 1 3 1 0 -8.2957841 1 0 0
## 2 4 1 0 -4.2957841 1 0 1
## 3 6 1 0 -0.2957841 0 1 1
## 4 7 1 0 2.7042159 0 1 1
## 5 8 1 0 -7.2957841 1 0 1
## 6 9 1 0 4.7042159 0 1 1
## htq1_06 htq1_7_10 htq1_8_9 htq1_11 htq1_12_36 htq1_13_18 htq1_14 htq1_15
## 1 0 0 0 0 0 0 0 0
## 2 1 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0 0
## 4 1 0 0 0 0 0 0 1
## 5 1 0 0 0 0 0 0 0
## 6 1 0 0 0 0 0 0 1
## htq1_16 htq1_17 htq1_19 htq1_20_24 htq1_21 htq1_22 htq1_23 htq1_25_to_29
## 1 0 0 0 0 0 0 0 0
## 2 0 1 0 0 0 0 0 0
## 3 0 1 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 1 0 0 0 0 0 1
## htq1_30_34 htq1_32 htq1_35 htq1_37 htq1_38 htq1_39 htq1_sum htq_ptsd_dsm
## 1 0 0 0 1 0 0 1 2.8750
## 2 0 0 0 0 1 1 5 2.5000
## 3 0 0 0 0 0 0 4 1.3125
## 4 0 0 0 0 0 0 5 2.6875
## 5 0 0 0 1 0 0 3 1.5000
## 6 0 0 0 1 1 0 8 2.3750
## htq_ptsd_total ace_2_1 ace_2_2 ace_3_1 ace_3_2 ace_3_3 ace_4_1 ace_4_2
## 1 2.666667 3 3 0 0 3 0 0
## 2 2.212121 4 4 0 0 2 0 1
## 3 1.272727 3 1 0 0 3 0 0
## 4 2.272727 4 4 0 0 0 0 0
## 5 1.363636 4 4 0 0 0 0 0
## 6 2.696970 2 4 0 0 0 0 0
## ace_4_3 ace_4_4 ace_4_5 ace_4_6 ace_4_7 ace_4_8 ace_5_1 ace_5_2 ace_5_3
## 1 0 0 0 2 0 0 2 0 3
## 2 1 0 0 0 0 0 2 2 3
## 3 1 1 1 0 0 0 2 0 2
## 4 0 0 0 2 2 0 0 0 2
## 5 0 0 0 2 1 0 3 0 2
## 6 0 0 0 3 3 3 3 0 3
## ace_5_4 ace_5_5 ace_5_6 ace_5_7 ace_5_8 ace_6_1 ace_6_2 ace_6_3 ace_7_1
## 1 2 0 0 0 0 0 <NA> 0 3
## 2 0 0 0 0 0 3 3 3 3
## 3 2 0 0 0 0 0 <NA> 0 3
## 4 0 0 0 0 0 3 3 2 3
## 5 0 0 0 0 0 0 <NA> 2 3
## 6 3 0 0 0 0 0 <NA> 3 3
## ace_7_2 ace_7_3 ace_e_t ace_2_1R ace_2_2R ace_f_1 ace_f_2 ace_f_en
## 1 3 3 9:10:48 AM 1 1 0 0 0
## 2 3 2 8:30:00 AM 0 0 0 0 0
## 3 3 3 7:30:00 AM 1 3 0 1 1
## 4 3 3 2023-03-19 13-41 0 0 0 0 0
## 5 3 3 7:00:58 AM 0 0 0 0 0
## 6 3 3 8:36:07 AM 2 0 1 0 1
## ace_f_pn_1 ace_f_pn_2 ace_f_pn_3 ace_f_pn ace_b_ah ace_b_dh ace_b_ih
## 1 0 0 1 1 0 0 0
## 2 0 0 0 0 0 1 1
## 3 0 0 1 1 0 0 1
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## ace_b_dd_1 ace_b_dd_2 ace_b_dd ace_f_hv_1 ace_f_hv_2 ace_f_hv_3 ace_f_hv
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 1 1 1 0 0 0 0
## 4 0 0 0 0 1 0 1
## 5 0 0 0 0 0 0 0
## 6 0 0 0 1 1 1 1
## ace_f_ea_1 ace_f_ea_2 ace_f_ea ace_f_pa_1 ace_f_pa_2 ace_f_pa ace_b_sa_1
## 1 0 0 0 1 0 1 0
## 2 0 0 0 1 0 1 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 1 0 1 0 0 0 0
## 6 1 0 1 1 1 1 0
## ace_b_sa_2 ace_b_sa_3 ace_b_sa_4 ace_b_sa ace_f_bu_1 ace_f_bu_2 ace_f_bu
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 1 1 1
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 1
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 1 1
## ace_f_cv_1 ace_f_cv_2 ace_f_cv_3 ace_f_cv ace_frequency ConflictTrauma
## 1 1 1 1 1 3 -2.3493940
## 2 1 1 0 1 5 0.2513647
## 3 1 1 1 1 5 0.2390247
## 4 1 1 1 1 3 0.4390420
## 5 1 1 1 1 2 -0.9548046
## 6 1 1 1 1 6 0.6314491
## Isolation_Loss ViolentVictimization Destruction_Injury WitnessViolence
## 1 -0.9307988 -0.6049161 -1.55697116 -0.2831504
## 2 -0.5007248 -0.5105563 -0.89249830 0.4582630
## 3 -0.7271977 -1.0211406 -0.67100446 -1.0430733
## 4 -0.4869772 -0.8023438 -0.01772711 -0.9085330
## 5 -0.7894892 -0.7051607 -1.26074699 -0.2554927
## 6 -0.1012761 0.1908505 0.34142527 0.6998006
## ChildAbuse ChildNeglectSexual ChildComViolence
## 1 -0.16468991 -0.10757488 0.83166654
## 2 0.09195881 -0.68873311 -0.01513485
## 3 -0.57054096 0.04951246 0.76862702
## 4 -0.23782091 -0.94025951 0.85256055
## 5 -0.10068003 -0.88215937 0.87140820
## 6 1.63846703 0.34604231 1.14614478Run a joint model of the factors
dataf <- data
data <- data %>% select(-c(ConflictTrauma ,Isolation_Loss, ViolentVictimization,Destruction_Injury ,WitnessViolence, ChildAbuse, ChildNeglectSexual, ChildComViolence))
model_j <- "
ConflictTrauma =~ htq1_05 + htq1_06 + htq1_17
Isolation_Loss =~ htq1_19 + htq1_21 + htq1_30_34
ViolentVictimization =~ htq1_7_10 + htq1_8_9 + htq1_11 + htq1_13_18 + htq1_14 + htq1_16 + htq1_20_24 + htq1_22 + htq1_25_to_29 + htq1_23
Destruction_Injury =~ htq1_04 + htq1_12_36 + htq1_15 + htq1_32 + htq1_35
WitnessViolence =~ htq1_39 + htq1_37 + htq1_38
htq1_04 ~~ htq1_15
ChildAbuse =~ ace_4_6 + ace_4_7 + ace_4_8 + ace_5_1 + ace_5_2 + ace_5_3 + ace_5_4 + ace_6_1 + ace_6_3
ChildNeglectSexual =~ ace_2_1R + ace_2_2R + ace_3_1 + ace_3_2 + ace_4_1 + ace_5_5 + ace_5_6 + ace_5_7 + ace_5_8
ChildComViolence =~ ace_7_1 + ace_7_2 + ace_7_3
ace_3_2 ~~ ace_4_1
ace_2_1R ~~ ace_2_2R
ace_4_6 ~~ ace_4_7
ace_4_7 ~~ ace_4_8
"
fit_modj <- cfa(model_j, data = data, auto.fix.first = FALSE, std.lv = TRUE, ordered = T)
tj <- fitMeasures(fit_modj, fit.measures = c("npar", "fmin", "chisq", "df", "pvalue", "srmr", "cfi.scaled", "tli.scaled", "rmsea.scaled"))
knitr::kable(tj, caption = "Model fit statistics Joint", digits = 3)| x | |
|---|---|
| npar | 153.00000000 |
| fmin | 0.70145004 |
| chisq | 4037.54644233 |
| df | 912.00000000 |
| pvalue | 0.00000000 |
| srmr | 0.08306356 |
| cfi.scaled | 0.93148410 |
| tli.scaled | 0.92562418 |
| rmsea.scaled | 0.03109901 |
## lavaan 0.6.17 ended normally after 37 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 153
##
## Used Total
## Number of observations 2878 2965
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 4037.546 3449.628
## Degrees of freedom 912 912
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.307
## Shift parameter 361.220
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 77430.812 38027.064
## Degrees of freedom 990 990
## P-value 0.000 0.000
## Scaling correction factor 2.064
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.959 0.931
## Tucker-Lewis Index (TLI) 0.956 0.926
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.035 0.031
## 90 Percent confidence interval - lower 0.033 0.030
## 90 Percent confidence interval - upper 0.036 0.032
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.083 0.083
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma =~
## htq1_05 0.746 0.022 33.294 0.000 0.746 0.746
## htq1_06 0.896 0.025 36.202 0.000 0.896 0.896
## htq1_17 0.869 0.020 43.297 0.000 0.869 0.869
## Isolation_Loss =~
## htq1_19 0.896 0.024 37.571 0.000 0.896 0.896
## htq1_21 0.936 0.025 37.945 0.000 0.936 0.936
## htq1_30_34 0.461 0.055 8.375 0.000 0.461 0.461
## ViolentVictimization =~
## htq1_7_10 0.641 0.029 22.223 0.000 0.641 0.641
## htq1_8_9 0.589 0.041 14.412 0.000 0.589 0.589
## htq1_11 0.786 0.020 38.607 0.000 0.786 0.786
## htq1_13_18 0.650 0.034 19.094 0.000 0.650 0.650
## htq1_14 0.838 0.019 44.801 0.000 0.838 0.838
## htq1_16 0.571 0.027 21.525 0.000 0.571 0.571
## htq1_20_24 0.671 0.039 17.206 0.000 0.671 0.671
## htq1_22 0.709 0.030 23.888 0.000 0.709 0.709
## htq1_25_to_29 0.489 0.041 11.911 0.000 0.489 0.489
## htq1_23 0.579 0.047 12.307 0.000 0.579 0.579
## Destruction_Injury =~
## htq1_04 0.592 0.025 23.260 0.000 0.592 0.592
## htq1_12_36 0.700 0.025 27.709 0.000 0.700 0.700
## htq1_15 0.683 0.023 29.599 0.000 0.683 0.683
## htq1_32 0.624 0.024 25.587 0.000 0.624 0.624
## htq1_35 0.689 0.025 27.600 0.000 0.689 0.689
## WitnessViolence =~
## htq1_39 0.685 0.020 34.876 0.000 0.685 0.685
## htq1_37 0.847 0.014 60.234 0.000 0.847 0.847
## htq1_38 0.927 0.013 70.755 0.000 0.927 0.927
## ChildAbuse =~
## ace_4_6 0.662 0.015 43.307 0.000 0.662 0.662
## ace_4_7 0.688 0.017 41.168 0.000 0.688 0.688
## ace_4_8 0.796 0.016 50.450 0.000 0.796 0.796
## ace_5_1 0.683 0.015 45.935 0.000 0.683 0.683
## ace_5_2 0.612 0.022 28.102 0.000 0.612 0.612
## ace_5_3 0.606 0.016 38.105 0.000 0.606 0.606
## ace_5_4 0.676 0.017 40.398 0.000 0.676 0.676
## ace_6_1 0.596 0.018 32.540 0.000 0.596 0.596
## ace_6_3 0.569 0.018 32.044 0.000 0.569 0.569
## ChildNeglectSexual =~
## ace_2_1R 0.321 0.034 9.347 0.000 0.321 0.321
## ace_2_2R 0.365 0.033 11.217 0.000 0.365 0.365
## ace_3_1 0.552 0.050 10.936 0.000 0.552 0.552
## ace_3_2 0.570 0.085 6.740 0.000 0.570 0.570
## ace_4_1 0.437 0.075 5.819 0.000 0.437 0.437
## ace_5_5 0.863 0.032 27.188 0.000 0.863 0.863
## ace_5_6 0.800 0.045 17.807 0.000 0.800 0.800
## ace_5_7 0.509 0.047 10.867 0.000 0.509 0.509
## ace_5_8 0.578 0.064 8.965 0.000 0.578 0.578
## ChildComViolence =~
## ace_7_1 0.708 0.022 32.273 0.000 0.708 0.708
## ace_7_2 0.798 0.018 45.275 0.000 0.798 0.798
## ace_7_3 0.875 0.018 49.209 0.000 0.875 0.875
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq1_04 ~~
## .htq1_15 0.354 0.027 13.227 0.000 0.354 0.602
## .ace_3_2 ~~
## .ace_4_1 0.653 0.073 9.011 0.000 0.653 0.884
## .ace_2_1R ~~
## .ace_2_2R 0.343 0.024 14.051 0.000 0.343 0.389
## .ace_4_6 ~~
## .ace_4_7 0.264 0.016 16.061 0.000 0.264 0.486
## .ace_4_7 ~~
## .ace_4_8 0.235 0.019 12.204 0.000 0.235 0.536
## ConflictTrauma ~~
## Isolation_Loss 0.429 0.038 11.154 0.000 0.429 0.429
## ViolentVctmztn 0.314 0.032 9.711 0.000 0.314 0.314
## Destrctn_Injry 0.708 0.027 26.210 0.000 0.708 0.708
## WitnessViolenc 0.523 0.026 20.374 0.000 0.523 0.523
## ChildAbuse 0.097 0.029 3.304 0.001 0.097 0.097
## ChildNeglctSxl -0.060 0.043 -1.413 0.158 -0.060 -0.060
## ChildComViolnc 0.274 0.030 9.125 0.000 0.274 0.274
## Isolation_Loss ~~
## ViolentVctmztn 0.500 0.033 15.213 0.000 0.500 0.500
## Destrctn_Injry 0.591 0.031 18.817 0.000 0.591 0.591
## WitnessViolenc 0.540 0.029 18.720 0.000 0.540 0.540
## ChildAbuse 0.260 0.030 8.584 0.000 0.260 0.260
## ChildNeglctSxl 0.356 0.045 7.958 0.000 0.356 0.356
## ChildComViolnc 0.087 0.036 2.452 0.014 0.087 0.087
## ViolentVictimization ~~
## Destrctn_Injry 0.679 0.026 26.108 0.000 0.679 0.679
## WitnessViolenc 0.762 0.019 39.582 0.000 0.762 0.762
## ChildAbuse 0.507 0.025 20.151 0.000 0.507 0.507
## ChildNeglctSxl 0.604 0.036 16.741 0.000 0.604 0.604
## ChildComViolnc 0.031 0.032 0.974 0.330 0.031 0.031
## Destruction_Injury ~~
## WitnessViolenc 0.736 0.021 34.689 0.000 0.736 0.736
## ChildAbuse 0.310 0.026 11.718 0.000 0.310 0.310
## ChildNeglctSxl 0.274 0.039 6.979 0.000 0.274 0.274
## ChildComViolnc 0.120 0.029 4.176 0.000 0.120 0.120
## WitnessViolence ~~
## ChildAbuse 0.340 0.024 14.147 0.000 0.340 0.340
## ChildNeglctSxl 0.385 0.035 11.130 0.000 0.385 0.385
## ChildComViolnc 0.215 0.027 8.095 0.000 0.215 0.215
## ChildAbuse ~~
## ChildNeglctSxl 0.485 0.034 14.450 0.000 0.485 0.485
## ChildComViolnc 0.409 0.021 19.437 0.000 0.409 0.409
## ChildNeglectSexual ~~
## ChildComViolnc 0.050 0.037 1.371 0.170 0.050 0.050
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## htq1_05|t1 -0.696 0.026 -27.246 0.000 -0.696 -0.696
## htq1_06|t1 -1.328 0.033 -40.691 0.000 -1.328 -1.328
## htq1_17|t1 -0.874 0.027 -32.465 0.000 -0.874 -0.874
## htq1_19|t1 1.130 0.030 38.065 0.000 1.130 1.130
## htq1_21|t1 1.202 0.031 39.198 0.000 1.202 1.202
## htq1_30_34|t1 1.651 0.040 41.733 0.000 1.651 1.651
## htq1_7_10|t1 1.301 0.032 40.425 0.000 1.301 1.301
## htq1_8_9|t1 1.720 0.041 41.466 0.000 1.720 1.720
## htq1_11|t1 0.989 0.028 35.290 0.000 0.989 0.989
## htq1_13_18|t1 1.621 0.039 41.799 0.000 1.621 1.621
## htq1_14|t1 1.118 0.030 37.866 0.000 1.118 1.118
## htq1_16|t1 0.968 0.028 34.810 0.000 0.968 0.968
## htq1_20_24|t1 1.838 0.045 40.640 0.000 1.838 1.838
## htq1_22|t1 1.422 0.034 41.403 0.000 1.422 1.422
## htq1_25_t_29|1 1.618 0.039 41.804 0.000 1.618 1.618
## htq1_23|t1 1.877 0.047 40.272 0.000 1.877 1.877
## htq1_04|t1 -0.535 0.025 -21.721 0.000 -0.535 -0.535
## htq1_12_36|t1 0.984 0.028 35.162 0.000 0.984 0.984
## htq1_15|t1 -0.021 0.023 -0.895 0.371 -0.021 -0.021
## htq1_32|t1 0.663 0.025 26.174 0.000 0.663 0.663
## htq1_35|t1 0.889 0.027 32.869 0.000 0.889 0.889
## htq1_39|t1 0.432 0.024 17.869 0.000 0.432 0.432
## htq1_37|t1 -0.032 0.023 -1.379 0.168 -0.032 -0.032
## htq1_38|t1 0.159 0.023 6.781 0.000 0.159 0.159
## ace_4_6|t1 -0.468 0.024 -19.230 0.000 -0.468 -0.468
## ace_4_6|t2 -0.320 0.024 -13.433 0.000 -0.320 -0.320
## ace_4_6|t3 0.352 0.024 14.730 0.000 0.352 0.352
## ace_4_7|t1 -0.129 0.023 -5.515 0.000 -0.129 -0.129
## ace_4_7|t2 0.051 0.023 2.162 0.031 0.051 0.051
## ace_4_7|t3 0.592 0.025 23.759 0.000 0.592 0.592
## ace_4_8|t1 0.456 0.024 18.789 0.000 0.456 0.456
## ace_4_8|t2 0.638 0.025 25.348 0.000 0.638 0.638
## ace_4_8|t3 1.057 0.029 36.718 0.000 1.057 1.057
## ace_5_1|t1 -0.229 0.024 -9.720 0.000 -0.229 -0.229
## ace_5_1|t2 -0.111 0.023 -4.733 0.000 -0.111 -0.111
## ace_5_1|t3 0.373 0.024 15.580 0.000 0.373 0.373
## ace_5_2|t1 0.670 0.025 26.389 0.000 0.670 0.670
## ace_5_2|t2 0.834 0.027 31.376 0.000 0.834 0.834
## ace_5_2|t3 1.358 0.033 40.956 0.000 1.358 1.358
## ace_5_3|t1 -0.660 0.025 -26.067 0.000 -0.660 -0.660
## ace_5_3|t2 -0.505 0.024 -20.624 0.000 -0.505 -0.505
## ace_5_3|t3 0.233 0.024 9.869 0.000 0.233 0.233
## ace_5_4|t1 0.347 0.024 14.544 0.000 0.347 0.347
## ace_5_4|t2 0.520 0.025 21.173 0.000 0.520 0.520
## ace_5_4|t3 0.975 0.028 34.970 0.000 0.975 0.975
## ace_6_1|t1 -0.152 0.023 -6.483 0.000 -0.152 -0.152
## ace_6_1|t2 -0.102 0.023 -4.360 0.000 -0.102 -0.102
## ace_6_1|t3 0.260 0.024 10.984 0.000 0.260 0.260
## ace_6_3|t1 -0.282 0.024 -11.912 0.000 -0.282 -0.282
## ace_6_3|t2 -0.180 0.024 -7.637 0.000 -0.180 -0.180
## ace_6_3|t3 0.412 0.024 17.095 0.000 0.412 0.412
## ace_2_1R|t1 0.234 0.024 9.906 0.000 0.234 0.234
## ace_2_1R|t2 0.865 0.027 32.228 0.000 0.865 0.865
## ace_2_1R|t3 1.439 0.035 41.495 0.000 1.439 1.439
## ace_2_1R|t4 2.016 0.052 38.627 0.000 2.016 2.016
## ace_2_2R|t1 0.021 0.023 0.895 0.371 0.021 0.021
## ace_2_2R|t2 0.622 0.025 24.808 0.000 0.622 0.622
## ace_2_2R|t3 1.099 0.029 37.520 0.000 1.099 1.099
## ace_2_2R|t4 1.705 0.041 41.539 0.000 1.705 1.705
## ace_3_1|t1 1.566 0.037 41.836 0.000 1.566 1.566
## ace_3_2|t1 2.163 0.059 36.392 0.000 2.163 2.163
## ace_4_1|t1 2.016 0.052 38.627 0.000 2.016 2.016
## ace_5_5|t1 1.525 0.036 41.793 0.000 1.525 1.525
## ace_5_6|t1 1.954 0.050 39.430 0.000 1.954 1.954
## ace_5_7|t1 1.602 0.038 41.825 0.000 1.602 1.602
## ace_5_8|t1 2.200 0.062 35.749 0.000 2.200 2.200
## ace_7_1|t1 -2.030 0.053 -38.441 0.000 -2.030 -2.030
## ace_7_1|t2 -1.811 0.044 -40.872 0.000 -1.811 -1.811
## ace_7_1|t3 -0.808 0.026 -30.654 0.000 -0.808 -0.808
## ace_7_2|t1 -1.566 0.037 -41.836 0.000 -1.566 -1.566
## ace_7_2|t2 -1.260 0.032 -39.958 0.000 -1.260 -1.260
## ace_7_2|t3 -0.416 0.024 -17.242 0.000 -0.416 -0.416
## ace_7_3|t1 -0.981 0.028 -35.098 0.000 -0.981 -0.981
## ace_7_3|t2 -0.824 0.026 -31.102 0.000 -0.824 -0.824
## ace_7_3|t3 -0.133 0.023 -5.664 0.000 -0.133 -0.133
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq1_05 0.443 0.443 0.443
## .htq1_06 0.197 0.197 0.197
## .htq1_17 0.245 0.245 0.245
## .htq1_19 0.197 0.197 0.197
## .htq1_21 0.125 0.125 0.125
## .htq1_30_34 0.788 0.788 0.788
## .htq1_7_10 0.589 0.589 0.589
## .htq1_8_9 0.653 0.653 0.653
## .htq1_11 0.383 0.383 0.383
## .htq1_13_18 0.577 0.577 0.577
## .htq1_14 0.297 0.297 0.297
## .htq1_16 0.673 0.673 0.673
## .htq1_20_24 0.550 0.550 0.550
## .htq1_22 0.498 0.498 0.498
## .htq1_25_to_29 0.761 0.761 0.761
## .htq1_23 0.665 0.665 0.665
## .htq1_04 0.650 0.650 0.650
## .htq1_12_36 0.510 0.510 0.510
## .htq1_15 0.533 0.533 0.533
## .htq1_32 0.611 0.611 0.611
## .htq1_35 0.526 0.526 0.526
## .htq1_39 0.531 0.531 0.531
## .htq1_37 0.282 0.282 0.282
## .htq1_38 0.140 0.140 0.140
## .ace_4_6 0.562 0.562 0.562
## .ace_4_7 0.527 0.527 0.527
## .ace_4_8 0.367 0.367 0.367
## .ace_5_1 0.534 0.534 0.534
## .ace_5_2 0.626 0.626 0.626
## .ace_5_3 0.633 0.633 0.633
## .ace_5_4 0.543 0.543 0.543
## .ace_6_1 0.644 0.644 0.644
## .ace_6_3 0.676 0.676 0.676
## .ace_2_1R 0.897 0.897 0.897
## .ace_2_2R 0.867 0.867 0.867
## .ace_3_1 0.695 0.695 0.695
## .ace_3_2 0.675 0.675 0.675
## .ace_4_1 0.809 0.809 0.809
## .ace_5_5 0.255 0.255 0.255
## .ace_5_6 0.360 0.360 0.360
## .ace_5_7 0.741 0.741 0.741
## .ace_5_8 0.666 0.666 0.666
## .ace_7_1 0.498 0.498 0.498
## .ace_7_2 0.363 0.363 0.363
## .ace_7_3 0.235 0.235 0.235
## ConflictTrauma 1.000 1.000 1.000
## Isolation_Loss 1.000 1.000 1.000
## ViolentVctmztn 1.000 1.000 1.000
## Destrctn_Injry 1.000 1.000 1.000
## WitnessViolenc 1.000 1.000 1.000
## ChildAbuse 1.000 1.000 1.000
## ChildNeglctSxl 1.000 1.000 1.000
## ChildComViolnc 1.000 1.000 1.000Joint model predicting DSM
Regular & SAM approach
SAM: Do not report yet, there are issues in the code regarding std.lv = T. In this case, it is setting the variance of age, a significant covariate, lavaan message board https://groups.google.com/g/lavaan/c/vF48T9yrtNU/m/2vXVO5a4BQAJ
model_jptsd <- "
ConflictTrauma =~ htq1_05 + htq1_06 + htq1_17
Isolation_Loss =~ htq1_19 + htq1_21 + htq1_30_34
ViolentVictimization =~ htq1_7_10 + htq1_8_9 + htq1_11 + htq1_13_18 + htq1_14 + htq1_16 + htq1_20_24 + htq1_22 + htq1_25_to_29 + htq1_23
Destruction_Injury =~ htq1_04 + htq1_12_36 + htq1_15 + htq1_32 + htq1_35
WitnessViolence =~ htq1_39 + htq1_37 + htq1_38
htq1_04 ~~ htq1_15
ChildAbuse =~ ace_4_6 + ace_4_7 + ace_4_8 + ace_5_1 + ace_5_2 + ace_5_3 + ace_5_4 + ace_6_1 + ace_6_3
ChildNeglectSexual =~ ace_2_1R + ace_2_2R + ace_3_1 + ace_3_2 + ace_4_1 + ace_5_5 + ace_5_6 + ace_5_7 + ace_5_8
ChildComViolence =~ ace_7_1 + ace_7_2 + ace_7_3
ace_3_2 ~~ ace_4_1
ace_2_1R ~~ ace_2_2R
ace_4_6 ~~ ace_4_7
ace_4_7 ~~ ace_4_8
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age
"
fit_modjptsd <- sem(model_jptsd, data = data, auto.fix.first = FALSE, std.lv = TRUE)
tj <- fitMeasures(fit_modjptsd, fit.measures = c("npar", "fmin", "chisq", "df", "pvalue", "srmr", "cfi.scaled", "tli.scaled", "rmsea.scaled"))
knitr::kable(tj, caption = "Model fit statistics PTSD", digits = 3)| x | |
|---|---|
| npar | 164.00000000 |
| fmin | 1.02668126 |
| chisq | 5585.14606621 |
| df | 994.00000000 |
| pvalue | 0.00000000 |
| srmr | 0.08598952 |
| cfi.scaled | 0.89483306 |
| tli.scaled | 0.89049519 |
| rmsea.scaled | 0.03641272 |
## lavaan 0.6.17 ended normally after 47 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 164
##
## Used Total
## Number of observations 2720 2965
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 5585.146 4577.454
## Degrees of freedom 994 994
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.336
## Shift parameter 398.341
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 71752.045 35108.956
## Degrees of freedom 1035 1035
## P-value 0.000 0.000
## Scaling correction factor 2.075
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.935 0.895
## Tucker-Lewis Index (TLI) 0.932 0.890
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.041 0.036
## 90 Percent confidence interval - lower 0.040 0.035
## 90 Percent confidence interval - upper 0.042 0.037
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.086 0.086
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma =~
## htq1_05 0.754 0.022 33.707 0.000 0.754 0.754
## htq1_06 0.908 0.026 35.558 0.000 0.908 0.908
## htq1_17 0.857 0.021 41.138 0.000 0.857 0.857
## Isolation_Loss =~
## htq1_19 0.878 0.026 34.324 0.000 0.878 0.878
## htq1_21 0.955 0.026 36.447 0.000 0.955 0.955
## htq1_30_34 0.334 0.061 5.440 0.000 0.334 0.334
## ViolentVictimization =~
## htq1_7_10 0.587 0.033 17.906 0.000 0.587 0.587
## htq1_8_9 0.561 0.044 12.703 0.000 0.561 0.561
## htq1_11 0.744 0.024 30.970 0.000 0.744 0.744
## htq1_13_18 0.582 0.039 14.927 0.000 0.582 0.582
## htq1_14 0.781 0.024 32.679 0.000 0.781 0.781
## htq1_16 0.524 0.029 18.062 0.000 0.524 0.524
## htq1_20_24 0.641 0.042 15.369 0.000 0.641 0.641
## htq1_22 0.665 0.033 19.977 0.000 0.665 0.665
## htq1_25_to_29 0.559 0.040 14.069 0.000 0.559 0.559
## htq1_23 0.546 0.049 11.135 0.000 0.546 0.546
## Destruction_Injury =~
## htq1_04 0.583 0.027 21.853 0.000 0.583 0.583
## htq1_12_36 0.704 0.026 26.799 0.000 0.704 0.704
## htq1_15 0.653 0.025 26.050 0.000 0.653 0.653
## htq1_32 0.619 0.026 24.165 0.000 0.619 0.619
## htq1_35 0.685 0.026 26.314 0.000 0.685 0.685
## WitnessViolence =~
## htq1_39 0.701 0.020 34.922 0.000 0.701 0.701
## htq1_37 0.847 0.015 55.611 0.000 0.847 0.847
## htq1_38 0.909 0.015 62.133 0.000 0.909 0.909
## ChildAbuse =~
## ace_4_6 0.681 0.015 45.038 0.000 0.681 0.681
## ace_4_7 0.697 0.017 41.407 0.000 0.697 0.697
## ace_4_8 0.790 0.016 48.677 0.000 0.790 0.790
## ace_5_1 0.691 0.015 46.063 0.000 0.691 0.691
## ace_5_2 0.604 0.022 27.108 0.000 0.604 0.604
## ace_5_3 0.607 0.016 37.212 0.000 0.607 0.607
## ace_5_4 0.670 0.017 38.645 0.000 0.670 0.670
## ace_6_1 0.603 0.019 32.462 0.000 0.603 0.603
## ace_6_3 0.559 0.018 30.477 0.000 0.559 0.559
## ChildNeglectSexual =~
## ace_2_1R 0.307 0.035 8.694 0.000 0.307 0.307
## ace_2_2R 0.347 0.034 10.299 0.000 0.347 0.347
## ace_3_1 0.544 0.051 10.638 0.000 0.544 0.544
## ace_3_2 0.576 0.086 6.679 0.000 0.576 0.576
## ace_4_1 0.451 0.077 5.848 0.000 0.451 0.451
## ace_5_5 0.857 0.033 26.231 0.000 0.857 0.857
## ace_5_6 0.802 0.047 17.099 0.000 0.802 0.802
## ace_5_7 0.558 0.046 12.083 0.000 0.558 0.558
## ace_5_8 0.618 0.063 9.765 0.000 0.618 0.618
## ChildComViolence =~
## ace_7_1 0.691 0.024 28.763 0.000 0.691 0.691
## ace_7_2 0.784 0.019 41.046 0.000 0.784 0.784
## ace_7_3 0.870 0.019 45.771 0.000 0.870 0.870
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## htq_ptsd_total ~
## ConflictTrauma -0.015 0.032 -0.451 0.652 -0.015 -0.027
## Isolation_Loss -0.001 0.023 -0.039 0.969 -0.001 -0.002
## ViolentVctmztn 0.106 0.043 2.472 0.013 0.106 0.198
## Destrctn_Injry 0.097 0.043 2.235 0.025 0.097 0.181
## WitnessViolenc -0.023 0.032 -0.719 0.472 -0.023 -0.043
## ChildAbuse 0.113 0.020 5.789 0.000 0.113 0.212
## ChildNeglctSxl 0.052 0.027 1.883 0.060 0.052 0.097
## ChildComViolnc 0.032 0.019 1.696 0.090 0.032 0.060
## q102b_guess_ag 0.018 0.002 10.054 0.000 0.018 0.193
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq1_04 ~~
## .htq1_15 0.373 0.028 13.241 0.000 0.373 0.605
## .ace_3_2 ~~
## .ace_4_1 0.645 0.076 8.458 0.000 0.645 0.884
## .ace_2_1R ~~
## .ace_2_2R 0.350 0.025 14.210 0.000 0.350 0.393
## .ace_4_6 ~~
## .ace_4_7 0.258 0.017 15.412 0.000 0.258 0.491
## .ace_4_7 ~~
## .ace_4_8 0.235 0.020 11.875 0.000 0.235 0.534
## ConflictTrauma ~~
## Isolation_Loss 0.408 0.040 10.127 0.000 0.408 0.408
## ViolentVctmztn 0.305 0.036 8.472 0.000 0.305 0.305
## Destrctn_Injry 0.712 0.028 25.059 0.000 0.712 0.712
## WitnessViolenc 0.523 0.026 19.770 0.000 0.523 0.523
## ChildAbuse 0.086 0.030 2.833 0.005 0.086 0.086
## ChildNeglctSxl -0.078 0.044 -1.784 0.074 -0.078 -0.078
## ChildComViolnc 0.288 0.031 9.311 0.000 0.288 0.288
## Isolation_Loss ~~
## ViolentVctmztn 0.479 0.036 13.333 0.000 0.479 0.479
## Destrctn_Injry 0.582 0.033 17.638 0.000 0.582 0.582
## WitnessViolenc 0.518 0.031 16.567 0.000 0.518 0.518
## ChildAbuse 0.250 0.031 8.025 0.000 0.250 0.250
## ChildNeglctSxl 0.323 0.047 6.908 0.000 0.323 0.323
## ChildComViolnc 0.115 0.036 3.181 0.001 0.115 0.115
## ViolentVictimization ~~
## Destrctn_Injry 0.647 0.030 21.663 0.000 0.647 0.647
## WitnessViolenc 0.734 0.023 31.702 0.000 0.734 0.734
## ChildAbuse 0.524 0.027 19.489 0.000 0.524 0.524
## ChildNeglctSxl 0.610 0.038 16.062 0.000 0.610 0.610
## ChildComViolnc 0.083 0.034 2.457 0.014 0.083 0.083
## Destruction_Injury ~~
## WitnessViolenc 0.730 0.023 31.981 0.000 0.730 0.730
## ChildAbuse 0.301 0.027 10.954 0.000 0.301 0.301
## ChildNeglctSxl 0.236 0.041 5.806 0.000 0.236 0.236
## ChildComViolnc 0.136 0.030 4.526 0.000 0.136 0.136
## WitnessViolence ~~
## ChildAbuse 0.324 0.025 12.883 0.000 0.324 0.324
## ChildNeglctSxl 0.348 0.036 9.633 0.000 0.348 0.348
## ChildComViolnc 0.232 0.028 8.328 0.000 0.232 0.232
## ChildAbuse ~~
## ChildNeglctSxl 0.464 0.035 13.429 0.000 0.464 0.464
## ChildComViolnc 0.417 0.022 19.109 0.000 0.417 0.417
## ChildNeglectSexual ~~
## ChildComViolnc 0.044 0.038 1.155 0.248 0.044 0.044
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq_ptsd_total 1.885 0.011 176.658 0.000 1.885 3.528
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## htq1_05|t1 -0.694 0.026 -26.374 0.000 -0.694 -0.694
## htq1_06|t1 -1.325 0.034 -39.520 0.000 -1.325 -1.325
## htq1_17|t1 -0.888 0.028 -31.800 0.000 -0.888 -0.888
## htq1_19|t1 1.137 0.031 36.709 0.000 1.137 1.137
## htq1_21|t1 1.206 0.032 37.940 0.000 1.206 1.206
## htq1_30_34|t1 1.809 0.054 33.662 0.000 1.809 1.809
## htq1_7_10|t1 1.369 0.036 37.895 0.000 1.369 1.369
## htq1_8_9|t1 1.771 0.048 37.109 0.000 1.771 1.771
## htq1_11|t1 1.035 0.031 33.444 0.000 1.035 1.035
## htq1_13_18|t1 1.679 0.043 38.791 0.000 1.679 1.679
## htq1_14|t1 1.244 0.037 33.899 0.000 1.244 1.244
## htq1_16|t1 0.994 0.029 33.727 0.000 0.994 0.994
## htq1_20_24|t1 1.903 0.051 37.145 0.000 1.903 1.903
## htq1_22|t1 1.477 0.039 38.078 0.000 1.477 1.477
## htq1_25_t_29|1 1.636 0.040 40.547 0.000 1.636 1.636
## htq1_23|t1 1.904 0.050 37.864 0.000 1.904 1.904
## htq1_04|t1 -0.531 0.026 -20.796 0.000 -0.531 -0.531
## htq1_12_36|t1 0.990 0.029 33.995 0.000 0.990 0.990
## htq1_15|t1 -0.026 0.024 -1.087 0.277 -0.026 -0.026
## htq1_32|t1 0.682 0.026 25.766 0.000 0.682 0.682
## htq1_35|t1 0.895 0.028 31.905 0.000 0.895 0.895
## htq1_39|t1 0.426 0.025 17.088 0.000 0.426 0.426
## htq1_37|t1 -0.047 0.024 -1.931 0.053 -0.047 -0.047
## htq1_38|t1 0.143 0.025 5.832 0.000 0.143 0.143
## ace_4_6|t1 -0.487 0.025 -19.388 0.000 -0.487 -0.487
## ace_4_6|t2 -0.340 0.025 -13.848 0.000 -0.340 -0.340
## ace_4_6|t3 0.333 0.025 13.590 0.000 0.333 0.333
## ace_4_7|t1 -0.136 0.024 -5.657 0.000 -0.136 -0.136
## ace_4_7|t2 0.039 0.024 1.631 0.103 0.039 0.039
## ace_4_7|t3 0.575 0.026 22.512 0.000 0.575 0.575
## ace_4_8|t1 0.447 0.025 17.913 0.000 0.447 0.447
## ace_4_8|t2 0.625 0.026 24.151 0.000 0.625 0.625
## ace_4_8|t3 1.044 0.030 35.353 0.000 1.044 1.044
## ace_5_1|t1 -0.262 0.024 -10.757 0.000 -0.262 -0.262
## ace_5_1|t2 -0.140 0.024 -5.820 0.000 -0.140 -0.140
## ace_5_1|t3 0.342 0.025 13.936 0.000 0.342 0.342
## ace_5_2|t1 0.667 0.026 25.547 0.000 0.667 0.667
## ace_5_2|t2 0.832 0.027 30.422 0.000 0.832 0.832
## ace_5_2|t3 1.353 0.034 39.738 0.000 1.353 1.353
## ace_5_3|t1 -0.667 0.026 -25.540 0.000 -0.667 -0.667
## ace_5_3|t2 -0.514 0.025 -20.322 0.000 -0.514 -0.514
## ace_5_3|t3 0.216 0.024 8.930 0.000 0.216 0.216
## ace_5_4|t1 0.340 0.025 13.829 0.000 0.340 0.340
## ace_5_4|t2 0.507 0.025 20.102 0.000 0.507 0.507
## ace_5_4|t3 0.966 0.029 33.738 0.000 0.966 0.966
## ace_6_1|t1 -0.164 0.024 -6.776 0.000 -0.164 -0.164
## ace_6_1|t2 -0.115 0.024 -4.777 0.000 -0.115 -0.115
## ace_6_1|t3 0.245 0.024 10.066 0.000 0.245 0.245
## ace_6_3|t1 -0.297 0.024 -12.169 0.000 -0.297 -0.297
## ace_6_3|t2 -0.192 0.024 -7.917 0.000 -0.192 -0.192
## ace_6_3|t3 0.404 0.025 16.293 0.000 0.404 0.404
## ace_2_1R|t1 0.232 0.024 9.553 0.000 0.232 0.232
## ace_2_1R|t2 0.867 0.028 31.468 0.000 0.867 0.867
## ace_2_1R|t3 1.448 0.036 40.351 0.000 1.448 1.448
## ace_2_1R|t4 2.018 0.054 37.437 0.000 2.018 2.018
## ace_2_2R|t1 0.014 0.024 0.597 0.550 0.014 0.014
## ace_2_2R|t2 0.622 0.026 24.104 0.000 0.622 0.622
## ace_2_2R|t3 1.101 0.030 36.484 0.000 1.101 1.101
## ace_2_2R|t4 1.697 0.042 40.371 0.000 1.697 1.697
## ace_3_1|t1 1.563 0.039 40.408 0.000 1.563 1.563
## ace_3_2|t1 2.146 0.061 35.398 0.000 2.146 2.146
## ace_4_1|t1 2.012 0.054 37.334 0.000 2.012 2.012
## ace_5_5|t1 1.525 0.038 39.980 0.000 1.525 1.525
## ace_5_6|t1 1.960 0.053 37.239 0.000 1.960 1.960
## ace_5_7|t1 1.609 0.040 40.583 0.000 1.609 1.609
## ace_5_8|t1 2.178 0.062 35.096 0.000 2.178 2.178
## ace_7_1|t1 -2.083 0.057 -36.807 0.000 -2.083 -2.083
## ace_7_1|t2 -1.878 0.048 -39.460 0.000 -1.878 -1.878
## ace_7_1|t3 -0.838 0.027 -30.487 0.000 -0.838 -0.838
## ace_7_2|t1 -1.651 0.040 -40.907 0.000 -1.651 -1.651
## ace_7_2|t2 -1.332 0.033 -39.802 0.000 -1.332 -1.332
## ace_7_2|t3 -0.451 0.025 -17.949 0.000 -0.451 -0.451
## ace_7_3|t1 -1.028 0.029 -35.129 0.000 -1.028 -1.028
## ace_7_3|t2 -0.869 0.028 -31.461 0.000 -0.869 -0.869
## ace_7_3|t3 -0.156 0.024 -6.462 0.000 -0.156 -0.156
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq1_05 0.432 0.432 0.432
## .htq1_06 0.176 0.176 0.176
## .htq1_17 0.265 0.265 0.265
## .htq1_19 0.229 0.229 0.229
## .htq1_21 0.087 0.087 0.087
## .htq1_30_34 0.889 0.889 0.889
## .htq1_7_10 0.656 0.656 0.656
## .htq1_8_9 0.686 0.686 0.686
## .htq1_11 0.447 0.447 0.447
## .htq1_13_18 0.662 0.662 0.662
## .htq1_14 0.390 0.390 0.390
## .htq1_16 0.726 0.726 0.726
## .htq1_20_24 0.589 0.589 0.589
## .htq1_22 0.558 0.558 0.558
## .htq1_25_to_29 0.687 0.687 0.687
## .htq1_23 0.702 0.702 0.702
## .htq1_04 0.661 0.661 0.661
## .htq1_12_36 0.505 0.505 0.505
## .htq1_15 0.574 0.574 0.574
## .htq1_32 0.617 0.617 0.617
## .htq1_35 0.531 0.531 0.531
## .htq1_39 0.509 0.509 0.509
## .htq1_37 0.283 0.283 0.283
## .htq1_38 0.174 0.174 0.174
## .ace_4_6 0.536 0.536 0.536
## .ace_4_7 0.514 0.514 0.514
## .ace_4_8 0.375 0.375 0.375
## .ace_5_1 0.522 0.522 0.522
## .ace_5_2 0.635 0.635 0.635
## .ace_5_3 0.632 0.632 0.632
## .ace_5_4 0.552 0.552 0.552
## .ace_6_1 0.636 0.636 0.636
## .ace_6_3 0.687 0.687 0.687
## .ace_2_1R 0.906 0.906 0.906
## .ace_2_2R 0.879 0.879 0.879
## .ace_3_1 0.704 0.704 0.704
## .ace_3_2 0.668 0.668 0.668
## .ace_4_1 0.797 0.797 0.797
## .ace_5_5 0.266 0.266 0.266
## .ace_5_6 0.358 0.358 0.358
## .ace_5_7 0.689 0.689 0.689
## .ace_5_8 0.617 0.617 0.617
## .ace_7_1 0.522 0.522 0.522
## .ace_7_2 0.385 0.385 0.385
## .ace_7_3 0.243 0.243 0.243
## .htq_ptsd_total 0.198 0.006 30.723 0.000 0.198 0.695
## ConflictTrauma 1.000 1.000 1.000
## Isolation_Loss 1.000 1.000 1.000
## ViolentVctmztn 1.000 1.000 1.000
## Destrctn_Injry 1.000 1.000 1.000
## WitnessViolenc 1.000 1.000 1.000
## ChildAbuse 1.000 1.000 1.000
## ChildNeglctSxl 1.000 1.000 1.000
## ChildComViolnc 1.000 1.000 1.000# fit <- sam(model_jptsd, data = data, cmd = "sem", mm.list = list("ConflictTrauma", "Isolation_Loss" , "ViolentVictimization", "Destruction_Injury", "WitnessViolence", "ChildAbuse", "ChildNeglectSexual", "ChildComViolence"), std.lv = T, auto.fix.first = F)
# summary(fit, fit.measures = TRUE, standardized = TRUE)
reg <- parameterestimates(fit_modjptsd)
#sam <- parameterestimates(fit)
reg %>% filter(op == "~")## lhs op rhs est se z pvalue ci.lower
## 1 htq_ptsd_total ~ ConflictTrauma -0.015 0.032 -0.451 0.652 -0.077
## 2 htq_ptsd_total ~ Isolation_Loss -0.001 0.023 -0.039 0.969 -0.046
## 3 htq_ptsd_total ~ ViolentVictimization 0.106 0.043 2.472 0.013 0.022
## 4 htq_ptsd_total ~ Destruction_Injury 0.097 0.043 2.235 0.025 0.012
## 5 htq_ptsd_total ~ WitnessViolence -0.023 0.032 -0.719 0.472 -0.085
## 6 htq_ptsd_total ~ ChildAbuse 0.113 0.020 5.789 0.000 0.075
## 7 htq_ptsd_total ~ ChildNeglectSexual 0.052 0.027 1.883 0.060 -0.002
## 8 htq_ptsd_total ~ ChildComViolence 0.032 0.019 1.696 0.090 -0.005
## 9 htq_ptsd_total ~ q102b_guess_age 0.018 0.002 10.054 0.000 0.014
## ci.upper
## 1 0.048
## 2 0.044
## 3 0.190
## 4 0.181
## 5 0.039
## 6 0.152
## 7 0.106
## 8 0.069
## 9 0.021Comparing invariance across males/females
Group model where everything is free to vary doesn’t run, trying alternative by constraining all parameters to be equal and then relaxing regressions. A model where regressions are “free” fits better than a model where regressions are the same.
# fit_modjptsdg <- sem(model_jptsd, data = data, auto.fix.first = FALSE, std.lv = TRUE, group = "respondent_cat")
# fit_modjptsdgr <- sem(model_jptsd, data = data, auto.fix.first = FALSE, std.lv = TRUE, group = "respondent_cat", group.equal = "regressions")
fit_modjptsdC <- sem(model_jptsd, data = data, auto.fix.first = FALSE, std.lv = TRUE, group = "respondent_cat", group.equal = c("loadings", "intercepts", "means", "thresholds", "regressions", "residuals", "residual.covariances", "lv.variances", "lv.covariances"))
fit_modjptsdR <- sem(model_jptsd, data = data, auto.fix.first = FALSE, std.lv = TRUE, group = "respondent_cat", group.equal = c("loadings", "intercepts", "means", "thresholds","residuals", "residual.covariances", "lv.variances", "lv.covariances"))
lavTestLRT(fit_modjptsdC, fit_modjptsdR, method = "satorra.bentler.2001")##
## Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit_modjptsdR 2098 12488
## fit_modjptsdC 2107 12740 33.746 9 0.00009897 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ################### Nested Model Comparison #########################
##
## Scaled Chi-Squared Difference Test (method = "satorra.2000")
##
## lavaan NOTE:
## The "Chisq" column contains standard test statistics, not the
## robust test that should be reported per model. A robust difference
## test is a function of two standard (not robust) statistics.
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## fit_modjptsdR 2098 12488
## fit_modjptsdC 2107 12740 88.311 9 0.000000000000003551 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled
## fit_modjptsdR 7579.580† 2098 .000 .044† .813†
## fit_modjptsdC 7642.827 2107 .000 .044 .811
## tli.scaled srmr
## fit_modjptsdR .815† .171†
## fit_modjptsdC .814 .171
##
## ################## Differences in Fit Indices #######################
## df.scaled rmsea.scaled cfi.scaled tli.scaled srmr
## fit_modjptsdC - fit_modjptsdR 9 0 -0.002 -0.001 0#
# fit_modjptsdf <- sem(model_jptsd, data = females, auto.fix.first = FALSE, std.lv = TRUE)
# summary(fit_modjptsdf, fit.measures = TRUE, standardized = TRUE)
#fitfL <- sam(model_jptsd, data = females, cmd = "sem", mm.list = list("ConflictTrauma", "Isolation_Loss" , "ViolentVictimization", "Destruction_Injury", "WitnessViolence", "ChildAbuse", "ChildNeglectSexual", "ChildComViolence"), std.lv = T, auto.fix.first = F)
#summary(fitfL, fit.measures = TRUE, standardized = TRUE)
reg <- parameterestimates(fit_modjptsdf)
#sam<- parameterestimates(fitfL)
reg %>% filter(op == "~")
#sam %>% filter(op == "~")
#fitm <- sam(model_jptsd, data = males, cmd = "sem", mm.list = list("ConflictTrauma", "Isolation_Loss" , "ViolentVictimization", "Destruction_Injury", "WitnessViolence", "ChildAbuse", "ChildNeglectSexual", "ChildComViolence"),mm.args = list(std.lv = T, auto.fix.first = F))
#summary(fitm, fit.measures = TRUE, standardized = TRUE)
#fitmL <- sam(model_jptsd, data = males, cmd = "sem", mm.list = list("ConflictTrauma", "Isolation_Loss" , "ViolentVictimization", "Destruction_Injury", "WitnessViolence", "ChildAbuse", "ChildNeglectSexual", "ChildComViolence"), std.lv = T, auto.fix.first = F)
#summary(fitmL, fit.measures = TRUE, standardized = TRUE)
reg <- parameterestimates(fitm)
#sam<- parameterestimates(fitmL)
reg %>% filter(op == "~")
#sam %>% filter(op == "~")
# fem <- parameterestimates(fitfL)
# mal <- parameterestimates(fitmL)
# t1 <- fem %>% filter(op == "~")
# knitr::kable(t1, caption = "Regressions (SAM) in Pregnant Women", digits = 3)
# t2 <- mal %>% filter(op == "~")
# knitr::kable(t2, caption = "Regressions (SAM) in Husbands", digits = 3)Final Regression model
With extracted factor scores
females <- dataf %>% filter(respondent_cat == 0)
model_obs <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~0*ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ChildAbuse ~~ 0*ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ 0*ChildComViolence
ChildNeglectSexual ~~ 0*Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs <- sem(model_obs, data = dataf,estimator = "ML", missing = "FIML.x", meanstructure = T, fixed.x = F)
summary(fit_obs, fit.measures = TRUE, standardized = TRUE, rsquare = T)## lavaan 0.6.17 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 45
##
## Number of observations 2965
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 1443.645
## Degrees of freedom 20
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 12386.842
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.885
## Tucker-Lewis Index (TLI) 0.740
##
## Robust Comparative Fit Index (CFI) 0.884
## Robust Tucker-Lewis Index (TLI) 0.739
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -39416.265
## Loglikelihood unrestricted model (H1) -38694.443
##
## Akaike (AIC) 78922.531
## Bayesian (BIC) 79192.289
## Sample-size adjusted Bayesian (SABIC) 79049.307
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.155
## 90 Percent confidence interval - lower 0.148
## 90 Percent confidence interval - upper 0.162
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Robust RMSEA 0.156
## 90 Percent confidence interval - lower 0.149
## 90 Percent confidence interval - upper 0.163
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.115
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## htq_ptsd_total ~
## ConflictTrauma 0.007 0.013 0.540 0.589 0.007 0.014
## Isolation_Loss 0.034 0.011 3.095 0.002 0.034 0.066
## ViolentVctmztn 0.088 0.016 5.503 0.000 0.088 0.166
## Destrctn_Injry 0.043 0.018 2.433 0.015 0.043 0.083
## WitnessViolenc 0.009 0.016 0.573 0.566 0.009 0.017
## ChildAbuse 0.126 0.011 11.068 0.000 0.126 0.243
## ChildNeglctSxl 0.049 0.011 4.620 0.000 0.049 0.094
## ChildComViolnc 0.015 0.010 1.420 0.155 0.015 0.028
## q102b_guess_ag 0.006 0.002 4.001 0.000 0.006 0.073
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma ~~
## Isolation_Loss 0.420 0.020 20.929 0.000 0.420 0.422
## ViolentVctmztn 0.309 0.019 16.529 0.000 0.309 0.317
## Destrctn_Injry 0.718 0.023 31.727 0.000 0.718 0.714
## WitnessViolenc 0.515 0.020 25.138 0.000 0.515 0.520
## ChildAbuse 0.163 0.020 8.263 0.000 0.163 0.163
## ChildNeglctSxl -0.101 0.015 -6.565 0.000 -0.101 -0.100
## ChildComViolnc 0.171 0.018 9.570 0.000 0.171 0.169
## Isolation_Loss ~~
## ViolentVctmztn 0.468 0.019 24.429 0.000 0.468 0.490
## Destrctn_Injry 0.569 0.021 26.980 0.000 0.569 0.578
## WitnessViolenc 0.512 0.020 25.698 0.000 0.512 0.529
## ChildAbuse 0.120 0.020 6.077 0.000 0.120 0.123
## ChildNeglctSxl 0.096 0.018 5.399 0.000 0.096 0.097
## ChildComViolnc 0.048 0.016 2.992 0.003 0.048 0.049
## ViolentVictimization ~~
## Destrctn_Injry 0.640 0.021 30.073 0.000 0.640 0.664
## WitnessViolenc 0.711 0.021 33.239 0.000 0.711 0.750
## ChildAbuse 0.223 0.018 12.270 0.000 0.223 0.233
## ChildNeglctSxl 0.174 0.016 10.977 0.000 0.174 0.180
## ChildComViolnc 0.000 0.000 0.000
## Destruction_Injury ~~
## WitnessViolenc 0.701 0.022 31.724 0.000 0.701 0.718
## ChildAbuse 0.218 0.018 12.153 0.000 0.218 0.220
## ChildComViolnc 0.079 0.014 5.740 0.000 0.079 0.079
## WitnessViolence ~~
## ChildAbuse 0.216 0.019 11.494 0.000 0.216 0.222
## ChildNeglctSxl 0.084 0.015 5.593 0.000 0.084 0.085
## ChildComViolnc 0.103 0.012 8.435 0.000 0.103 0.105
## ChildAbuse ~~
## ChildNeglctSxl 0.000 0.000 0.000
## ChildComViolnc 0.394 0.019 20.274 0.000 0.394 0.396
## ChildNeglectSexual ~~
## ChildComViolnc 0.000 0.000 0.000
## Destruction_Injury ~~
## ChildNeglctSxl 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma 0.000 0.000 0.000
## Isolation_Loss 0.000 0.000 0.000
## ViolentVctmztn 0.000 0.000 0.000
## Destrctn_Injry 0.000 0.000 0.000
## WitnessViolenc 0.000 0.000 0.000
## ChildAbuse 0.000 0.000 0.000
## ChildNeglctSxl 0.000 0.000 0.000
## ChildComViolnc 0.000 0.000 0.000
## .htq_ptsd_total 1.882 0.009 216.705 0.000 1.882 3.651
## q102b_guess_ag -0.000 0.107 -0.000 1.000 -0.000 -0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq_ptsd_total 0.211 0.006 37.350 0.000 0.211 0.793
## ConflictTrauma 1.019 0.027 37.800 0.000 1.019 1.000
## Isolation_Loss 0.974 0.025 38.802 0.000 0.974 1.000
## ViolentVctmztn 0.934 0.024 39.613 0.000 0.934 1.000
## Destrctn_Injry 0.993 0.026 38.601 0.000 0.993 1.000
## WitnessViolenc 0.960 0.025 38.825 0.000 0.960 1.000
## ChildAbuse 0.987 0.025 38.768 0.000 0.987 1.000
## ChildNeglctSxl 1.000 0.026 38.047 0.000 1.000 1.000
## ChildComViolnc 1.000 0.026 38.063 0.000 1.000 1.000
## q102b_guess_ag 34.098 0.886 38.503 0.000 34.098 1.000
##
## R-Square:
## Estimate
## htq_ptsd_total 0.207fit_obsg <- sem(model_obs, data = dataf,estimator = "ML", missing = "FIML.x", meanstructure = T, fixed.x = F, group = "respondent_cat")
#summary(fit_obsg, fit.measures = TRUE, standardized = TRUE)
out1 <- compareFit(fit_obs,fit_obsg, nested = F)
summary(out1)## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit_obs 1443.645† 20 .000 .155† .885† .740† .115† 78922.531 79192.289
## fit_obsg 2467.582 40 .000 .202 .778 .500 .184 77250.674† 77790.191†fit_obsgr <- sem(model_obs, data = dataf,estimator = "ML", missing = "FIML.x", meanstructure = T, fixed.x = F, group = "respondent_cat", group.equal = "regressions")
out1 <- compareFit(fit_obsgr,fit_obsg, nested = T)
summary(out1)## ################### Nested Model Comparison #########################
##
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
## fit_obsg 40 77251 77790 2467.6
## fit_obsgr 49 77311 77797 2546.3 78.698 0.072275 9 0.0000000000002931
##
## fit_obsg
## fit_obsgr ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit_obsg 2467.582† 40 .000 .202 .778† .500 .184† 77250.674† 77790.191†
## fit_obsgr 2546.279 49 .000 .185† .771 .580† .185 77311.372 77796.937
##
## ################## Differences in Fit Indices #######################
## df rmsea cfi tli srmr aic bic
## fit_obsgr - fit_obsg 9 -0.017 -0.006 0.08 0.001 60.698 6.746## lavaan 0.6.17 ended normally after 85 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 90
##
## Number of observations per group:
## 0 2323
## 1 642
## Number of missing patterns per group:
## 0 6
## 1 6
##
## Model Test User Model:
##
## Test statistic 2467.582
## Degrees of freedom 40
## P-value (Chi-square) 0.000
## Test statistic for each group:
## 0 1409.370
## 1 1058.212
##
## Model Test Baseline Model:
##
## Test statistic 11007.823
## Degrees of freedom 90
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.778
## Tucker-Lewis Index (TLI) 0.500
##
## Robust Comparative Fit Index (CFI) 0.778
## Robust Tucker-Lewis Index (TLI) 0.500
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -38535.337
## Loglikelihood unrestricted model (H1) -37301.546
##
## Akaike (AIC) 77250.674
## Bayesian (BIC) 77790.191
## Sample-size adjusted Bayesian (SABIC) 77504.227
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.202
## 90 Percent confidence interval - lower 0.196
## 90 Percent confidence interval - upper 0.209
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Robust RMSEA 0.204
## 90 Percent confidence interval - lower 0.197
## 90 Percent confidence interval - upper 0.211
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.184
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
##
## Group 1 [0]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## htq_ptsd_total ~
## ConflictTrauma -0.008 0.016 -0.484 0.628 -0.008 -0.015
## Isolation_Loss 0.063 0.014 4.640 0.000 0.063 0.120
## ViolentVctmztn 0.086 0.022 3.921 0.000 0.086 0.130
## Destrctn_Injry 0.048 0.021 2.281 0.023 0.048 0.090
## WitnessViolenc 0.013 0.017 0.737 0.461 0.013 0.023
## ChildAbuse 0.101 0.013 7.695 0.000 0.101 0.192
## ChildNeglctSxl 0.083 0.014 6.146 0.000 0.083 0.146
## ChildComViolnc -0.006 0.011 -0.562 0.574 -0.006 -0.013
## q102b_guess_ag 0.009 0.002 4.440 0.000 0.009 0.087
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma ~~
## Isolation_Loss 0.439 0.022 20.416 0.000 0.439 0.466
## ViolentVctmztn 0.250 0.016 15.329 0.000 0.250 0.331
## Destrctn_Injry 0.666 0.023 28.483 0.000 0.666 0.722
## WitnessViolenc 0.489 0.021 23.180 0.000 0.489 0.541
## ChildAbuse 0.125 0.021 5.892 0.000 0.125 0.133
## ChildNeglctSxl -0.066 0.015 -4.441 0.000 -0.066 -0.075
## ChildComViolnc 0.195 0.020 9.803 0.000 0.195 0.197
## Isolation_Loss ~~
## ViolentVctmztn 0.385 0.017 22.396 0.000 0.385 0.513
## Destrctn_Injry 0.579 0.022 25.776 0.000 0.579 0.631
## WitnessViolenc 0.490 0.021 23.413 0.000 0.490 0.545
## ChildAbuse 0.118 0.021 5.543 0.000 0.118 0.126
## ChildNeglctSxl 0.090 0.017 5.362 0.000 0.090 0.104
## ChildComViolnc 0.083 0.018 4.630 0.000 0.083 0.084
## ViolentVictimization ~~
## Destrctn_Injry 0.486 0.018 26.623 0.000 0.486 0.661
## WitnessViolenc 0.503 0.018 28.013 0.000 0.503 0.699
## ChildAbuse 0.095 0.016 5.863 0.000 0.095 0.126
## ChildNeglctSxl 0.106 0.013 8.092 0.000 0.106 0.152
## ChildComViolnc 0.000 0.000 0.000
## Destruction_Injury ~~
## WitnessViolenc 0.608 0.022 27.576 0.000 0.608 0.691
## ChildAbuse 0.141 0.019 7.504 0.000 0.141 0.153
## ChildComViolnc 0.091 0.015 5.998 0.000 0.091 0.095
## WitnessViolence ~~
## ChildAbuse 0.132 0.020 6.607 0.000 0.132 0.147
## ChildNeglctSxl 0.040 0.015 2.656 0.008 0.040 0.048
## ChildComViolnc 0.135 0.014 9.312 0.000 0.135 0.143
## ChildAbuse ~~
## ChildNeglctSxl 0.000 0.000 0.000
## ChildComViolnc 0.367 0.022 16.714 0.000 0.367 0.372
## ChildNeglectSexual ~~
## ChildComViolnc 0.000 0.000 0.000
## Destruction_Injury ~~
## ChildNeglctSxl 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma 0.000 0.000 0.000
## Isolation_Loss 0.000 0.000 0.000
## ViolentVctmztn 0.000 0.000 0.000
## Destrctn_Injry 0.000 0.000 0.000
## WitnessViolenc 0.000 0.000 0.000
## ChildAbuse 0.000 0.000 0.000
## ChildNeglctSxl 0.000 0.000 0.000
## ChildComViolnc 0.000 0.000 0.000
## .htq_ptsd_total 1.909 0.011 170.871 0.000 1.909 3.733
## q102b_guess_ag -1.157 0.107 -10.806 0.000 -1.157 -0.224
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq_ptsd_total 0.214 0.006 32.985 0.000 0.214 0.819
## ConflictTrauma 0.949 0.028 33.923 0.000 0.949 1.000
## Isolation_Loss 0.937 0.027 34.520 0.000 0.937 1.000
## ViolentVctmztn 0.601 0.017 34.739 0.000 0.601 1.000
## Destrctn_Injry 0.898 0.026 34.236 0.000 0.898 1.000
## WitnessViolenc 0.862 0.025 34.496 0.000 0.862 1.000
## ChildAbuse 0.941 0.028 33.942 0.000 0.941 1.000
## ChildNeglctSxl 0.807 0.024 33.712 0.000 0.807 1.000
## ChildComViolnc 1.032 0.031 33.712 0.000 1.032 1.000
## q102b_guess_ag 26.639 0.782 34.081 0.000 26.639 1.000
##
## R-Square:
## Estimate
## htq_ptsd_total 0.181
##
##
## Group 2 [1]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## htq_ptsd_total ~
## ConflictTrauma 0.026 0.024 1.104 0.270 0.026 0.053
## Isolation_Loss -0.053 0.019 -2.741 0.006 -0.053 -0.100
## ViolentVctmztn 0.143 0.028 5.170 0.000 0.143 0.371
## Destrctn_Injry -0.008 0.031 -0.268 0.789 -0.008 -0.017
## WitnessViolenc 0.056 0.036 1.580 0.114 0.056 0.115
## ChildAbuse 0.145 0.024 6.097 0.000 0.145 0.277
## ChildNeglctSxl 0.012 0.016 0.737 0.461 0.012 0.027
## ChildComViolnc 0.094 0.023 4.099 0.000 0.094 0.158
## q102b_guess_ag 0.005 0.003 1.823 0.068 0.005 0.057
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma ~~
## Isolation_Loss 0.349 0.052 6.725 0.000 0.349 0.293
## ViolentVctmztn 0.558 0.070 7.991 0.000 0.558 0.340
## Destrctn_Injry 0.912 0.064 14.204 0.000 0.912 0.699
## WitnessViolenc 0.627 0.059 10.706 0.000 0.627 0.484
## ChildAbuse 0.281 0.050 5.610 0.000 0.281 0.233
## ChildNeglctSxl -0.215 0.050 -4.318 0.000 -0.215 -0.146
## ChildComViolnc 0.071 0.039 1.802 0.072 0.071 0.067
## Isolation_Loss ~~
## ViolentVctmztn 0.789 0.068 11.649 0.000 0.789 0.515
## Destrctn_Injry 0.536 0.056 9.651 0.000 0.536 0.440
## WitnessViolenc 0.602 0.054 11.114 0.000 0.602 0.498
## ChildAbuse 0.106 0.049 2.157 0.031 0.106 0.094
## ChildNeglctSxl 0.143 0.059 2.422 0.015 0.143 0.104
## ChildComViolnc -0.092 0.035 -2.659 0.008 -0.092 -0.093
## ViolentVictimization ~~
## Destrctn_Injry 1.210 0.083 14.575 0.000 1.210 0.720
## WitnessViolenc 1.451 0.087 16.642 0.000 1.451 0.871
## ChildAbuse 0.728 0.064 11.365 0.000 0.728 0.469
## ChildNeglctSxl 0.334 0.059 5.643 0.000 0.334 0.176
## ChildComViolnc 0.000 0.000 0.000
## Destruction_Injury ~~
## WitnessViolenc 1.047 0.068 15.440 0.000 1.047 0.789
## ChildAbuse 0.487 0.049 9.914 0.000 0.487 0.394
## ChildComViolnc 0.022 0.030 0.731 0.465 0.022 0.020
## WitnessViolence ~~
## ChildAbuse 0.552 0.051 10.824 0.000 0.552 0.450
## ChildNeglctSxl 0.185 0.041 4.465 0.000 0.185 0.123
## ChildComViolnc -0.016 0.021 -0.756 0.449 -0.016 -0.015
## ChildAbuse ~~
## ChildNeglctSxl 0.000 0.000 0.000
## ChildComViolnc 0.492 0.041 12.134 0.000 0.492 0.490
## ChildNeglectSexual ~~
## ChildComViolnc 0.000 0.000 0.000
## Destruction_Injury ~~
## ChildNeglctSxl 0.000 0.000 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma 0.000 0.000 0.000
## Isolation_Loss 0.000 0.000 0.000
## ViolentVctmztn 0.000 0.000 0.000
## Destrctn_Injry 0.000 0.000 0.000
## WitnessViolenc 0.000 0.000 0.000
## ChildAbuse 0.000 0.000 0.000
## ChildNeglctSxl 0.000 0.000 0.000
## ChildComViolnc 0.000 0.000 0.000
## .htq_ptsd_total 1.816 0.031 59.137 0.000 1.816 3.239
## q102b_guess_ag 4.187 0.246 17.052 0.000 4.187 0.673
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq_ptsd_total 0.166 0.010 17.466 0.000 0.166 0.529
## ConflictTrauma 1.276 0.076 16.890 0.000 1.276 1.000
## Isolation_Loss 1.113 0.062 17.892 0.000 1.113 1.000
## ViolentVctmztn 2.110 0.117 18.096 0.000 2.110 1.000
## Destrctn_Injry 1.337 0.075 17.841 0.000 1.337 1.000
## WitnessViolenc 1.316 0.074 17.678 0.000 1.316 1.000
## ChildAbuse 1.144 0.060 19.208 0.000 1.144 1.000
## ChildNeglctSxl 1.705 0.097 17.657 0.000 1.705 1.000
## ChildComViolnc 0.883 0.050 17.676 0.000 0.883 1.000
## q102b_guess_ag 38.711 2.161 17.916 0.000 38.711 1.000
##
## R-Square:
## Estimate
## htq_ptsd_total 0.471f4 <- parameterEstimates(fit_obsg, standardized = TRUE)
#f4 %>% filter(op == "~")
#t2 <- mal %>% filter(op == "~")
#knitr::kable(t2, caption = "Regressions (SAM) in Husbands", digits = 3)
#t2 <- fem %>% filter(op == "~")
#knitr::kable(t2, caption = "Regressions (SAM) in PW", digits = 3)
t3 <- f4 %>% filter(op == "~")
knitr::kable(t3, caption = "Regressions with extracted factors in PW & H", digits = 3)| lhs | op | rhs | block | group | est | se | z | pvalue | ci.lower | ci.upper | std.lv | std.all | std.nox |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| htq_ptsd_total | ~ | ConflictTrauma | 1 | 1 | -0.008 | 0.016 | -0.484 | 0.628 | -0.039 | 0.024 | -0.008 | -0.015 | -0.015 |
| htq_ptsd_total | ~ | Isolation_Loss | 1 | 1 | 0.063 | 0.014 | 4.640 | 0.000 | 0.037 | 0.090 | 0.063 | 0.120 | 0.120 |
| htq_ptsd_total | ~ | ViolentVictimization | 1 | 1 | 0.086 | 0.022 | 3.921 | 0.000 | 0.043 | 0.128 | 0.086 | 0.130 | 0.130 |
| htq_ptsd_total | ~ | Destruction_Injury | 1 | 1 | 0.048 | 0.021 | 2.281 | 0.023 | 0.007 | 0.090 | 0.048 | 0.090 | 0.090 |
| htq_ptsd_total | ~ | WitnessViolence | 1 | 1 | 0.013 | 0.017 | 0.737 | 0.461 | -0.021 | 0.047 | 0.013 | 0.023 | 0.023 |
| htq_ptsd_total | ~ | ChildAbuse | 1 | 1 | 0.101 | 0.013 | 7.695 | 0.000 | 0.075 | 0.127 | 0.101 | 0.192 | 0.192 |
| htq_ptsd_total | ~ | ChildNeglectSexual | 1 | 1 | 0.083 | 0.014 | 6.146 | 0.000 | 0.057 | 0.110 | 0.083 | 0.146 | 0.146 |
| htq_ptsd_total | ~ | ChildComViolence | 1 | 1 | -0.006 | 0.011 | -0.562 | 0.574 | -0.029 | 0.016 | -0.006 | -0.013 | -0.013 |
| htq_ptsd_total | ~ | q102b_guess_age | 1 | 1 | 0.009 | 0.002 | 4.440 | 0.000 | 0.005 | 0.012 | 0.009 | 0.087 | 0.017 |
| htq_ptsd_total | ~ | ConflictTrauma | 2 | 2 | 0.026 | 0.024 | 1.104 | 0.270 | -0.020 | 0.073 | 0.026 | 0.053 | 0.053 |
| htq_ptsd_total | ~ | Isolation_Loss | 2 | 2 | -0.053 | 0.019 | -2.741 | 0.006 | -0.091 | -0.015 | -0.053 | -0.100 | -0.100 |
| htq_ptsd_total | ~ | ViolentVictimization | 2 | 2 | 0.143 | 0.028 | 5.170 | 0.000 | 0.089 | 0.197 | 0.143 | 0.371 | 0.371 |
| htq_ptsd_total | ~ | Destruction_Injury | 2 | 2 | -0.008 | 0.031 | -0.268 | 0.789 | -0.069 | 0.052 | -0.008 | -0.017 | -0.017 |
| htq_ptsd_total | ~ | WitnessViolence | 2 | 2 | 0.056 | 0.036 | 1.580 | 0.114 | -0.014 | 0.126 | 0.056 | 0.115 | 0.115 |
| htq_ptsd_total | ~ | ChildAbuse | 2 | 2 | 0.145 | 0.024 | 6.097 | 0.000 | 0.098 | 0.192 | 0.145 | 0.277 | 0.277 |
| htq_ptsd_total | ~ | ChildNeglectSexual | 2 | 2 | 0.012 | 0.016 | 0.737 | 0.461 | -0.019 | 0.042 | 0.012 | 0.027 | 0.027 |
| htq_ptsd_total | ~ | ChildComViolence | 2 | 2 | 0.094 | 0.023 | 4.099 | 0.000 | 0.049 | 0.139 | 0.094 | 0.158 | 0.158 |
| htq_ptsd_total | ~ | q102b_guess_age | 2 | 2 | 0.005 | 0.003 | 1.823 | 0.068 | 0.000 | 0.011 | 0.005 | 0.057 | 0.009 |
Interactions (Females)
Tested each possible interaction & set aside significant ones. Reported only those that survive false discovery rate.
datafemale = dataf %>% filter(respondent_cat == 0)
model_obsMod <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age + Destruction_Injury:ChildNeglectSexual
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ConflictTrauma ~~ 0*ChildNeglectSexual
ChildAbuse ~~ ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ ChildComViolence
ChildNeglectSexual ~~ Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs <- sem(model_obsMod, data = datafemale,estimator = "MLR", missing = "FIML.x", meanstructure = T, fixed.x = F)
#summary(fit_obs, fit.measures = T, standardize=T)
f4 <- parameterEstimates(fit_obs, standardized = TRUE)
f4 %>% filter(op == "~")## lhs op rhs est se z
## 1 htq_ptsd_total ~ ConflictTrauma -0.008 0.017 -0.470
## 2 htq_ptsd_total ~ Isolation_Loss 0.066 0.014 4.781
## 3 htq_ptsd_total ~ ViolentVictimization 0.084 0.021 3.936
## 4 htq_ptsd_total ~ Destruction_Injury 0.046 0.021 2.205
## 5 htq_ptsd_total ~ WitnessViolence 0.012 0.017 0.670
## 6 htq_ptsd_total ~ ChildAbuse 0.099 0.013 7.454
## 7 htq_ptsd_total ~ ChildNeglectSexual 0.080 0.014 5.665
## 8 htq_ptsd_total ~ ChildComViolence -0.005 0.011 -0.446
## 9 htq_ptsd_total ~ q102b_guess_age 0.009 0.002 4.485
## 10 htq_ptsd_total ~ Destruction_Injury:ChildNeglectSexual -0.027 0.012 -2.335
## pvalue ci.lower ci.upper std.lv std.all std.nox
## 1 0.638 -0.041 0.025 -0.008 -0.015 -0.015
## 2 0.000 0.039 0.093 0.066 0.124 0.124
## 3 0.000 0.042 0.125 0.084 0.126 0.126
## 4 0.027 0.005 0.088 0.046 0.084 0.084
## 5 0.503 -0.022 0.045 0.012 0.021 0.021
## 6 0.000 0.073 0.126 0.099 0.185 0.185
## 7 0.000 0.052 0.107 0.080 0.137 0.137
## 8 0.655 -0.027 0.017 -0.005 -0.010 -0.010
## 9 0.000 0.005 0.012 0.009 0.085 0.017
## 10 0.020 -0.051 -0.004 -0.027 -0.044 -0.053Models with significant interactions (Females)
Isolation x Sexual Abuse
#Isolation Sexual
model_obsMod1 <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age + Isolation_Loss:ChildNeglectSexual
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ConflictTrauma ~~ 0*ChildNeglectSexual
ChildAbuse ~~ ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ ChildComViolence
ChildNeglectSexual ~~ Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs1 <- sem(model_obsMod1, data = datafemale,estimator = "MLR", missing = "FIML.x", meanstructure = T, fixed.x = F)
datafemale %>% select(Isolation_Loss,ChildNeglectSexual, htq_ptsd_total) %>% report_table()## Variable | n_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | percentage_Missing
## -------------------------------------------------------------------------------------------------------------------
## Isolation_Loss | 2323 | -0.12 | 0.97 | | 0.52 | -1.34 | 3.01 | 1.52 | 1.51 | 0.60
## ChildNeglectSexual | 2323 | -0.18 | 0.88 | | 0.77 | -1.62 | 3.39 | 1.07 | 1.35 | 2.20
## htq_ptsd_total | 2323 | 1.83 | 0.52 | | 0.54 | 1.00 | 3.79 | 0.46 | -0.24 | 5.98result2way2 <- probe2WayMC(fit_obs1, nameX=c("Isolation_Loss","ChildNeglectSexual","Isolation_Loss:ChildNeglectSexual"),
nameY="htq_ptsd_total", modVar="ChildNeglectSexual", valProbe = c(-0.7, 0, 0.7))
result2way2## $SimpleIntcept
## ChildNeglectSexual est se z pvalue
## 1 -0.7 1.855 0.014 131.562 0
## 2 0.0 1.913 0.011 169.114 0
## 3 0.7 1.970 0.016 123.864 0
##
## $SimpleSlope
## ChildNeglectSexual est se z pvalue
## 1 -0.7 0.083 0.016 5.180 0.000
## 2 0.0 0.067 0.014 4.888 0.000
## 3 0.7 0.051 0.016 3.266 0.001plotProbe(result2way2, xlim = c(-1.34, 3.01), xlab = "Isolation",
ylab = "PTSD Symptoms", legend = TRUE)
values_probe2 <- seq(from=-1.62, to = 3.39, by = 0.1)
result2way3 <- probe2WayMC(fit_obs1, nameX=c("Isolation_Loss","ChildNeglectSexual","Isolation_Loss:ChildNeglectSexual"),
nameY="htq_ptsd_total", modVar="ChildNeglectSexual", valProbe=values_probe2)
result2way3## $SimpleIntcept
## ChildNeglectSexual est se z pvalue
## 1 -1.62 1.779 0.024 73.236 0
## 2 -1.52 1.787 0.023 77.551 0
## 3 -1.42 1.795 0.022 82.280 0
## 4 -1.32 1.804 0.021 87.468 0
## 5 -1.22 1.812 0.019 93.158 0
## 6 -1.12 1.820 0.018 99.391 0
## 7 -1.02 1.828 0.017 106.195 0
## 8 -0.92 1.837 0.016 113.574 0
## 9 -0.82 1.845 0.015 121.493 0
## 10 -0.72 1.853 0.014 129.852 0
## 11 -0.62 1.861 0.013 138.455 0
## 12 -0.52 1.870 0.013 146.975 0
## 13 -0.42 1.878 0.012 154.941 0
## 14 -0.32 1.886 0.012 161.752 0
## 15 -0.22 1.894 0.011 166.760 0
## 16 -0.12 1.903 0.011 169.416 0
## 17 -0.02 1.911 0.011 169.429 0
## 18 0.08 1.919 0.012 166.863 0
## 19 0.18 1.927 0.012 162.111 0
## 20 0.28 1.936 0.012 155.760 0
## 21 0.38 1.944 0.013 148.433 0
## 22 0.48 1.952 0.014 140.670 0
## 23 0.58 1.960 0.015 132.879 0
## 24 0.68 1.969 0.016 125.329 0
## 25 0.78 1.977 0.017 118.183 0
## 26 0.88 1.985 0.018 111.518 0
## 27 0.98 1.993 0.019 105.361 0
## 28 1.08 2.002 0.020 99.706 0
## 29 1.18 2.010 0.021 94.527 0
## 30 1.28 2.018 0.022 89.791 0
## 31 1.38 2.026 0.024 85.460 0
## 32 1.48 2.035 0.025 81.496 0
## 33 1.58 2.043 0.026 77.863 0
## 34 1.68 2.051 0.028 74.528 0
## 35 1.78 2.059 0.029 71.460 0
## 36 1.88 2.068 0.030 68.633 0
## 37 1.98 2.076 0.031 66.021 0
## 38 2.08 2.084 0.033 63.604 0
## 39 2.18 2.092 0.034 61.361 0
## 40 2.28 2.101 0.035 59.277 0
## 41 2.38 2.109 0.037 57.335 0
## 42 2.48 2.117 0.038 55.523 0
## 43 2.58 2.126 0.039 53.828 0
## 44 2.68 2.134 0.041 52.241 0
## 45 2.78 2.142 0.042 50.751 0
## 46 2.88 2.150 0.044 49.351 0
## 47 2.98 2.159 0.045 48.032 0
## 48 3.08 2.167 0.046 46.789 0
## 49 3.18 2.175 0.048 45.614 0
## 50 3.28 2.183 0.049 44.504 0
## 51 3.38 2.192 0.050 43.452 0
##
## $SimpleSlope
## ChildNeglectSexual est se z pvalue
## 1 -1.62 0.104 0.023 4.491 0.000
## 2 -1.52 0.102 0.022 4.569 0.000
## 3 -1.42 0.099 0.021 4.650 0.000
## 4 -1.32 0.097 0.021 4.732 0.000
## 5 -1.22 0.095 0.020 4.814 0.000
## 6 -1.12 0.093 0.019 4.895 0.000
## 7 -1.02 0.090 0.018 4.974 0.000
## 8 -0.92 0.088 0.017 5.047 0.000
## 9 -0.82 0.086 0.017 5.114 0.000
## 10 -0.72 0.083 0.016 5.170 0.000
## 11 -0.62 0.081 0.016 5.211 0.000
## 12 -0.52 0.079 0.015 5.234 0.000
## 13 -0.42 0.077 0.015 5.233 0.000
## 14 -0.32 0.074 0.014 5.204 0.000
## 15 -0.22 0.072 0.014 5.143 0.000
## 16 -0.12 0.070 0.014 5.049 0.000
## 17 -0.02 0.067 0.014 4.918 0.000
## 18 0.08 0.065 0.014 4.754 0.000
## 19 0.18 0.063 0.014 4.559 0.000
## 20 0.28 0.060 0.014 4.337 0.000
## 21 0.38 0.058 0.014 4.096 0.000
## 22 0.48 0.056 0.015 3.841 0.000
## 23 0.58 0.054 0.015 3.580 0.000
## 24 0.68 0.051 0.015 3.318 0.001
## 25 0.78 0.049 0.016 3.060 0.002
## 26 0.88 0.047 0.017 2.810 0.005
## 27 0.98 0.044 0.017 2.569 0.010
## 28 1.08 0.042 0.018 2.341 0.019
## 29 1.18 0.040 0.019 2.125 0.034
## 30 1.28 0.038 0.020 1.922 0.055
## 31 1.38 0.035 0.020 1.733 0.083
## 32 1.48 0.033 0.021 1.556 0.120
## 33 1.58 0.031 0.022 1.391 0.164
## 34 1.68 0.028 0.023 1.237 0.216
## 35 1.78 0.026 0.024 1.094 0.274
## 36 1.88 0.024 0.025 0.961 0.337
## 37 1.98 0.022 0.026 0.837 0.403
## 38 2.08 0.019 0.027 0.721 0.471
## 39 2.18 0.017 0.028 0.613 0.540
## 40 2.28 0.015 0.029 0.513 0.608
## 41 2.38 0.012 0.030 0.418 0.676
## 42 2.48 0.010 0.031 0.330 0.741
## 43 2.58 0.008 0.032 0.248 0.804
## 44 2.68 0.006 0.033 0.170 0.865
## 45 2.78 0.003 0.034 0.097 0.923
## 46 2.88 0.001 0.035 0.028 0.977
## 47 2.98 -0.001 0.036 -0.036 0.971
## 48 3.08 -0.004 0.037 -0.097 0.923
## 49 3.18 -0.006 0.038 -0.155 0.877
## 50 3.28 -0.008 0.039 -0.209 0.834
## 51 3.38 -0.010 0.040 -0.261 0.794# PRETTIER GRAPHS
fiti <- lm(htq_ptsd_total ~ ConflictTrauma + Isolation_Loss*ChildNeglectSexual + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence + q102b_guess_age, data = datafemale)
ss <- sim_slopes(fiti, pred = Isolation_Loss, modx = ChildNeglectSexual, johnson_neyman = TRUE,control.fdr = TRUE)
ss ## JOHNSON-NEYMAN INTERVAL
##
## When ChildNeglectSexual is INSIDE the interval [-28.70, 1.17], the slope of
## Isolation_Loss is p < .05.
##
## Note: The range of observed values of ChildNeglectSexual is [-1.62, 3.39]
##
## Interval calculated using false discovery rate adjusted t = 2.20
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of Isolation_Loss when ChildNeglectSexual = -1.0586168 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.09 0.02 4.63 0.00
##
## Slope of Isolation_Loss when ChildNeglectSexual = -0.1736839 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.07 0.01 4.92 0.00
##
## Slope of Isolation_Loss when ChildNeglectSexual = 0.7112489 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.05 0.02 3.31 0.00## Loading required namespace: broom.mixed
probe_interaction(fiti, pred = Isolation_Loss, modx = ChildNeglectSexual, cond.int = TRUE,
interval = TRUE, jnplot = TRUE)## JOHNSON-NEYMAN INTERVAL
##
## When ChildNeglectSexual is OUTSIDE the interval [1.29, 110.82], the slope
## of Isolation_Loss is p < .05.
##
## Note: The range of observed values of ChildNeglectSexual is [-1.62, 3.39]
## SIMPLE SLOPES ANALYSIS
##
## When ChildNeglectSexual = -1.0586168 (- 1 SD):
##
## Est. S.E. t val. p
## ----------------------------- ------ ------ -------- ------
## Slope of Isolation_Loss 0.09 0.02 4.63 0.00
## Conditional intercept 1.75 0.02 111.58 0.00
##
## When ChildNeglectSexual = -0.1736839 (Mean):
##
## Est. S.E. t val. p
## ----------------------------- ------ ------ -------- ------
## Slope of Isolation_Loss 0.07 0.01 4.92 0.00
## Conditional intercept 1.83 0.01 178.59 0.00
##
## When ChildNeglectSexual = 0.7112489 (+ 1 SD):
##
## Est. S.E. t val. p
## ----------------------------- ------ ------ -------- ------
## Slope of Isolation_Loss 0.05 0.02 3.31 0.00
## Conditional intercept 1.90 0.02 119.47 0.00
p <- interact_plot(fiti, pred = Isolation_Loss, modx = ChildNeglectSexual, plot.points = TRUE, modx.values = "terciles", colors = "blue", point.size = 1, point.alpha = 0.25, rug = T, jitter = 0.2, x.label = "Isolation & loss", y.label = "PTSD symptoms", interval = T, legend.main = "Neglect &\nSexual abuse", vary.lty = F)## Medians of each tercile of ChildNeglectSexual are -0.993, -0.319, 0.53p + theme_2 + scale_y_continuous(limits = c(1.3, 2.3)) + scale_x_continuous(n.breaks = 10) + theme(legend.title = element_text(size = 14), legend.text = element_text(size=12)) 
Migration violence x Sexual Abuse
model_obsMod2 <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age + Destruction_Injury:ChildNeglectSexual
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ConflictTrauma ~~ 0*ChildNeglectSexual
ChildAbuse ~~ ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ ChildComViolence
ChildNeglectSexual ~~ Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs2 <- sem(model_obsMod2, data = datafemale,estimator = "MLR", missing = "FIML.x", meanstructure = T, fixed.x = F)
datafemale %>% select(Destruction_Injury,ChildNeglectSexual) %>% report_table()## Variable | n_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | percentage_Missing
## -------------------------------------------------------------------------------------------------------------------
## Destruction_Injury | 2323 | -0.19 | 0.93 | | 0.97 | -1.94 | 2.44 | 0.25 | -0.30 | 0.60
## ChildNeglectSexual | 2323 | -0.18 | 0.88 | | 0.77 | -1.62 | 3.39 | 1.07 | 1.35 | 2.20result2way2 <- probe2WayMC(fit_obs2, nameX=c("Destruction_Injury","ChildNeglectSexual","Destruction_Injury:ChildNeglectSexual"),
nameY="htq_ptsd_total", modVar="ChildNeglectSexual", valProbe = c(-1, 0, 0.8))
result2way2## $SimpleIntcept
## ChildNeglectSexual est se z pvalue
## 1 -1.0 1.832 0.017 107.830 0
## 2 0.0 1.911 0.011 170.628 0
## 3 0.8 1.975 0.017 118.285 0
##
## $SimpleSlope
## ChildNeglectSexual est se z pvalue
## 1 -1.0 0.074 0.023 3.169 0.002
## 2 0.0 0.046 0.021 2.205 0.027
## 3 0.8 0.024 0.024 1.028 0.304plotProbe(result2way2, xlim = c(-1.94, 2.44), xlab = "Migration Violence (SD)",
ylab = "PTSD Symptoms", legend = TRUE)
values_probe2 <- seq(from=-1.62, to = 3.39, by = 0.1)
result2way3 <- probe2WayMC(fit_obs2, nameX=c("Destruction_Injury","ChildNeglectSexual","Destruction_Injury:ChildNeglectSexual"),
nameY="htq_ptsd_total", modVar="ChildNeglectSexual", valProbe=values_probe2)
result2way3## $SimpleIntcept
## ChildNeglectSexual est se z pvalue
## 1 -1.62 1.782 0.024 73.530 0
## 2 -1.52 1.790 0.023 77.833 0
## 3 -1.42 1.798 0.022 82.548 0
## 4 -1.32 1.806 0.021 87.721 0
## 5 -1.22 1.814 0.019 93.396 0
## 6 -1.12 1.822 0.018 99.613 0
## 7 -1.02 1.830 0.017 106.402 0
## 8 -0.92 1.838 0.016 113.772 0
## 9 -0.82 1.846 0.015 121.692 0
## 10 -0.72 1.854 0.014 130.070 0
## 11 -0.62 1.862 0.013 138.719 0
## 12 -0.52 1.870 0.013 147.326 0
## 13 -0.42 1.878 0.012 155.430 0
## 14 -0.32 1.886 0.012 162.437 0
## 15 -0.22 1.894 0.011 167.691 0
## 16 -0.12 1.902 0.011 170.620 0
## 17 -0.02 1.909 0.011 170.895 0
## 18 0.08 1.917 0.011 168.545 0
## 19 0.18 1.925 0.012 163.935 0
## 20 0.28 1.933 0.012 157.647 0
## 21 0.38 1.941 0.013 150.313 0
## 22 0.48 1.949 0.014 142.491 0
## 23 0.58 1.957 0.015 134.606 0
## 24 0.68 1.965 0.015 126.946 0
## 25 0.78 1.973 0.016 119.682 0
## 26 0.88 1.981 0.018 112.900 0
## 27 0.98 1.989 0.019 106.631 0
## 28 1.08 1.997 0.020 100.872 0
## 29 1.18 2.005 0.021 95.597 0
## 30 1.28 2.013 0.022 90.774 0
## 31 1.38 2.021 0.023 86.364 0
## 32 1.48 2.029 0.025 82.328 0
## 33 1.58 2.037 0.026 78.631 0
## 34 1.68 2.045 0.027 75.237 0
## 35 1.78 2.053 0.028 72.116 0
## 36 1.88 2.061 0.030 69.241 0
## 37 1.98 2.069 0.031 66.586 0
## 38 2.08 2.076 0.032 64.129 0
## 39 2.18 2.084 0.034 61.850 0
## 40 2.28 2.092 0.035 59.732 0
## 41 2.38 2.100 0.036 57.761 0
## 42 2.48 2.108 0.038 55.921 0
## 43 2.58 2.116 0.039 54.200 0
## 44 2.68 2.124 0.040 52.589 0
## 45 2.78 2.132 0.042 51.078 0
## 46 2.88 2.140 0.043 49.657 0
## 47 2.98 2.148 0.044 48.320 0
## 48 3.08 2.156 0.046 47.059 0
## 49 3.18 2.164 0.047 45.868 0
## 50 3.28 2.172 0.049 44.742 0
## 51 3.38 2.180 0.050 43.675 0
##
## $SimpleSlope
## ChildNeglectSexual est se z pvalue
## 1 -1.62 0.091 0.027 3.329 0.001
## 2 -1.52 0.088 0.027 3.319 0.001
## 3 -1.42 0.085 0.026 3.303 0.001
## 4 -1.32 0.083 0.025 3.282 0.001
## 5 -1.22 0.080 0.025 3.255 0.001
## 6 -1.12 0.077 0.024 3.220 0.001
## 7 -1.02 0.074 0.023 3.178 0.001
## 8 -0.92 0.072 0.023 3.128 0.002
## 9 -0.82 0.069 0.022 3.068 0.002
## 10 -0.72 0.066 0.022 2.998 0.003
## 11 -0.62 0.063 0.022 2.918 0.004
## 12 -0.52 0.061 0.021 2.828 0.005
## 13 -0.42 0.058 0.021 2.728 0.006
## 14 -0.32 0.055 0.021 2.617 0.009
## 15 -0.22 0.052 0.021 2.497 0.013
## 16 -0.12 0.050 0.021 2.369 0.018
## 17 -0.02 0.047 0.021 2.233 0.026
## 18 0.08 0.044 0.021 2.091 0.037
## 19 0.18 0.041 0.021 1.945 0.052
## 20 0.28 0.039 0.022 1.796 0.073
## 21 0.38 0.036 0.022 1.645 0.100
## 22 0.48 0.033 0.022 1.495 0.135
## 23 0.58 0.030 0.023 1.346 0.178
## 24 0.68 0.028 0.023 1.199 0.230
## 25 0.78 0.025 0.024 1.056 0.291
## 26 0.88 0.022 0.024 0.918 0.359
## 27 0.98 0.019 0.025 0.784 0.433
## 28 1.08 0.017 0.025 0.656 0.512
## 29 1.18 0.014 0.026 0.533 0.594
## 30 1.28 0.011 0.027 0.416 0.677
## 31 1.38 0.008 0.028 0.305 0.760
## 32 1.48 0.006 0.028 0.199 0.842
## 33 1.58 0.003 0.029 0.099 0.921
## 34 1.68 0.000 0.030 0.005 0.996
## 35 1.78 -0.003 0.031 -0.085 0.933
## 36 1.88 -0.005 0.032 -0.169 0.866
## 37 1.98 -0.008 0.033 -0.249 0.803
## 38 2.08 -0.011 0.033 -0.324 0.746
## 39 2.18 -0.014 0.034 -0.396 0.692
## 40 2.28 -0.016 0.035 -0.463 0.643
## 41 2.38 -0.019 0.036 -0.527 0.599
## 42 2.48 -0.022 0.037 -0.587 0.557
## 43 2.58 -0.025 0.038 -0.644 0.520
## 44 2.68 -0.027 0.039 -0.697 0.486
## 45 2.78 -0.030 0.040 -0.748 0.454
## 46 2.88 -0.033 0.041 -0.797 0.426
## 47 2.98 -0.036 0.042 -0.843 0.399
## 48 3.08 -0.038 0.043 -0.886 0.375
## 49 3.18 -0.041 0.044 -0.928 0.354
## 50 3.28 -0.044 0.045 -0.967 0.334
## 51 3.38 -0.047 0.046 -1.004 0.315# 1 -1.18 0.113 0.054 2.087 0.037
# 2 -1.13 0.111 0.054 2.061 0.039
# 3 -1.08 0.108 0.053 2.034 0.042
# 4 -1.03 0.106 0.053 2.006 0.045
# 5 -0.98 0.103 0.052 1.977 0.048
fiti <- lm(htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury*ChildNeglectSexual + WitnessViolence + ChildAbuse + ChildComViolence + q102b_guess_age, data = datafemale)
ss <- sim_slopes(fiti, pred = Destruction_Injury, modx = ChildNeglectSexual, johnson_neyman = TRUE,control.fdr = TRUE)
ss ## JOHNSON-NEYMAN INTERVAL
##
## When ChildNeglectSexual is INSIDE the interval [-20.25, -0.23], the slope
## of Destruction_Injury is p < .05.
##
## Note: The range of observed values of ChildNeglectSexual is [-1.62, 3.39]
##
## Interval calculated using false discovery rate adjusted t = 2.46
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of Destruction_Injury when ChildNeglectSexual = -1.0586168 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.08 0.02 3.09 0.00
##
## Slope of Destruction_Injury when ChildNeglectSexual = -0.1736839 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.05 0.02 2.39 0.02
##
## Slope of Destruction_Injury when ChildNeglectSexual = 0.7112489 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.03 0.02 1.15 0.25
probe_interaction(fiti, pred = Destruction_Injury, modx = ChildNeglectSexual, cond.int = TRUE, interval = TRUE, jnplot = TRUE)## JOHNSON-NEYMAN INTERVAL
##
## When ChildNeglectSexual is OUTSIDE the interval [0.15, 13.66], the slope of
## Destruction_Injury is p < .05.
##
## Note: The range of observed values of ChildNeglectSexual is [-1.62, 3.39]
## SIMPLE SLOPES ANALYSIS
##
## When ChildNeglectSexual = -1.0586168 (- 1 SD):
##
## Est. S.E. t val. p
## --------------------------------- ------ ------ -------- ------
## Slope of Destruction_Injury 0.08 0.02 3.09 0.00
## Conditional intercept 1.75 0.02 111.70 0.00
##
## When ChildNeglectSexual = -0.1736839 (Mean):
##
## Est. S.E. t val. p
## --------------------------------- ------ ------ -------- ------
## Slope of Destruction_Injury 0.05 0.02 2.39 0.02
## Conditional intercept 1.83 0.01 180.34 0.00
##
## When ChildNeglectSexual = 0.7112489 (+ 1 SD):
##
## Est. S.E. t val. p
## --------------------------------- ------ ------ -------- ------
## Slope of Destruction_Injury 0.03 0.02 1.15 0.25
## Conditional intercept 1.90 0.02 120.45 0.00
#interact_plot(fiti, pred = Destruction_Injury, modx = ChildNeglectSexual, plot.points = TRUE)
d <- interact_plot(fiti, pred = Destruction_Injury, modx = ChildNeglectSexual, plot.points = TRUE, modx.values = "terciles", colors = "blue", point.size = 1, point.alpha = 0.25, rug = T, jitter = 0.2, x.label = "Destruction & Injury", y.label = "PTSD symptoms", interval = T, legend.main = "Neglect &\nSexual abuse", vary.lty = F)## Medians of each tercile of ChildNeglectSexual are -0.993, -0.319, 0.53
Interactions (Males)
Tested each possible interaction & set aside significant ones. Reported only those that survive false discovery rate.
datamale = dataf %>% filter(respondent_cat == 1)
model_obsMod <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age + WitnessViolence:ChildComViolence
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + 0*ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ConflictTrauma ~~ ChildNeglectSexual
ChildAbuse ~~ ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ 0*ChildComViolence
ChildNeglectSexual ~~ Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs <- sem(model_obsMod, data = datamale,estimator = "MLR", missing = "FIML.x", meanstructure = T, fixed.x = F)
summary(fit_obs, fit.measures = T, standardize=T)## lavaan 0.6.17 ended normally after 66 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 51
##
## Number of observations 642
## Number of missing patterns 6
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 1504.871 1277.755
## Degrees of freedom 26 26
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.178
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 3069.497 2774.792
## Degrees of freedom 54 54
## P-value 0.000 0.000
## Scaling correction factor 1.106
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.510 0.540
## Tucker-Lewis Index (TLI) -0.019 0.044
##
## Robust Comparative Fit Index (CFI) 0.512
## Robust Tucker-Lewis Index (TLI) -0.013
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -9765.398 -9765.398
## Scaling correction factor 1.034
## for the MLR correction
## Loglikelihood unrestricted model (H1) -9012.963 -9012.963
## Scaling correction factor 1.083
## for the MLR correction
##
## Akaike (AIC) 19632.797 19632.797
## Bayesian (BIC) 19860.491 19860.491
## Sample-size adjusted Bayesian (SABIC) 19698.569 19698.569
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.298 0.274
## 90 Percent confidence interval - lower 0.285 0.262
## 90 Percent confidence interval - upper 0.311 0.286
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 1.000 1.000
##
## Robust RMSEA 0.300
## 90 Percent confidence interval - lower 0.285
## 90 Percent confidence interval - upper 0.315
## P-value H_0: Robust RMSEA <= 0.050 0.000
## P-value H_0: Robust RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.470 0.470
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## htq_ptsd_total ~
## ConflictTrauma 0.023 0.024 0.959 0.338 0.023 0.045
## Isolation_Loss -0.053 0.020 -2.582 0.010 -0.053 -0.098
## ViolentVctmztn 0.146 0.027 5.383 0.000 0.146 0.389
## Destrctn_Injry -0.006 0.029 -0.219 0.826 -0.006 -0.013
## WitnessViolenc 0.067 0.035 1.902 0.057 0.067 0.139
## ChildAbuse 0.137 0.024 5.713 0.000 0.137 0.260
## ChildNeglctSxl 0.010 0.016 0.639 0.523 0.010 0.023
## ChildComViolnc 0.042 0.036 1.188 0.235 0.042 0.069
## q102b_guess_ag 0.005 0.003 2.014 0.044 0.005 0.055
## WtnssVlnc:ChCV 0.057 0.025 2.288 0.022 0.057 0.113
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma ~~
## Isolation_Loss 0.357 0.042 8.590 0.000 0.357 0.305
## ViolentVctmztn 0.548 0.060 9.129 0.000 0.548 0.324
## Destrctn_Injry 0.856 0.049 17.571 0.000 0.856 0.673
## WitnessViolenc 0.603 0.048 12.551 0.000 0.603 0.459
## ChildAbuse 0.231 0.044 5.293 0.000 0.231 0.194
## ChildComViolnc 0.117 0.039 2.984 0.003 0.117 0.113
## Isolation_Loss ~~
## ViolentVctmztn 0.872 0.067 12.921 0.000 0.872 0.533
## Destrctn_Injry 0.577 0.046 12.620 0.000 0.577 0.468
## WitnessViolenc 0.659 0.051 12.975 0.000 0.659 0.518
## ChildAbuse 0.205 0.036 5.689 0.000 0.205 0.177
## ChildNeglctSxl 0.330 0.053 6.188 0.000 0.330 0.238
## ChildComViolnc 0.000 0.000 0.000
## ViolentVictimization ~~
## Destrctn_Injry 1.314 0.068 19.246 0.000 1.314 0.738
## WitnessViolenc 1.620 0.069 23.553 0.000 1.620 0.883
## ChildAbuse 0.925 0.064 14.431 0.000 0.925 0.554
## ChildNeglctSxl 0.985 0.082 12.087 0.000 0.985 0.491
## ChildComViolnc 0.143 0.045 3.182 0.001 0.143 0.099
## Destruction_Injury ~~
## WitnessViolenc 1.104 0.056 19.756 0.000 1.104 0.799
## ChildAbuse 0.533 0.046 11.697 0.000 0.533 0.424
## ChildComViolnc 0.106 0.035 3.006 0.003 0.106 0.098
## WitnessViolence ~~
## ChildAbuse 0.671 0.047 14.192 0.000 0.671 0.518
## ChildNeglctSxl 0.704 0.060 11.715 0.000 0.704 0.452
## ChildComViolnc 0.083 0.034 2.417 0.016 0.083 0.074
## ConflictTrauma ~~
## ChildNeglctSxl 0.087 0.059 1.481 0.139 0.087 0.061
## ChildAbuse ~~
## ChildNeglctSxl 0.620 0.051 12.218 0.000 0.620 0.438
## ChildComViolnc 0.525 0.038 13.838 0.000 0.525 0.515
## ChildNeglectSexual ~~
## ChildComViolnc 0.000 0.000 0.000
## Destruction_Injury ~~
## ChildNeglctSxl 0.517 0.059 8.760 0.000 0.517 0.342
## q102b_guess_age ~~
## WtnssVlnc:ChCV -0.221 0.301 -0.736 0.462 -0.221 -0.031
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ConflictTrauma 0.000 0.000 0.000
## Isolation_Loss 0.000 0.000 0.000
## ViolentVctmztn 0.000 0.000 0.000
## Destrctn_Injry 0.000 0.000 0.000
## WitnessViolenc 0.000 0.000 0.000
## ChildAbuse 0.000 0.000 0.000
## ChildNeglctSxl 0.000 0.000 0.000
## ChildComViolnc 0.000 0.000 0.000
## .htq_ptsd_total 1.800 0.031 57.819 0.000 1.800 3.133
## q102b_guess_ag 4.187 0.246 17.052 0.000 4.187 0.673
## WtnssVlnc:ChCV 0.080 0.046 1.750 0.080 0.080 0.070
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .htq_ptsd_total 0.165 0.009 18.270 0.000 0.165 0.500
## ConflictTrauma 1.208 0.054 22.246 0.000 1.208 1.000
## Isolation_Loss 1.133 0.080 14.235 0.000 1.133 1.000
## ViolentVctmztn 2.363 0.096 24.639 0.000 2.363 1.000
## Destrctn_Injry 1.341 0.062 21.659 0.000 1.341 1.000
## WitnessViolenc 1.425 0.052 27.213 0.000 1.425 1.000
## ChildAbuse 1.179 0.061 19.188 0.000 1.179 1.000
## ChildNeglctSxl 1.704 0.114 14.938 0.000 1.704 1.000
## ChildComViolnc 0.884 0.037 23.797 0.000 0.884 1.000
## q102b_guess_ag 38.711 3.341 11.588 0.000 38.711 1.000
## WtnssVlnc:ChCV 1.300 0.080 16.343 0.000 1.300 1.000## lhs op rhs est se z
## 1 htq_ptsd_total ~ ConflictTrauma 0.023 0.024 0.959
## 2 htq_ptsd_total ~ Isolation_Loss -0.053 0.020 -2.582
## 3 htq_ptsd_total ~ ViolentVictimization 0.146 0.027 5.383
## 4 htq_ptsd_total ~ Destruction_Injury -0.006 0.029 -0.219
## 5 htq_ptsd_total ~ WitnessViolence 0.067 0.035 1.902
## 6 htq_ptsd_total ~ ChildAbuse 0.137 0.024 5.713
## 7 htq_ptsd_total ~ ChildNeglectSexual 0.010 0.016 0.639
## 8 htq_ptsd_total ~ ChildComViolence 0.042 0.036 1.188
## 9 htq_ptsd_total ~ q102b_guess_age 0.005 0.003 2.014
## 10 htq_ptsd_total ~ WitnessViolence:ChildComViolence 0.057 0.025 2.288
## pvalue ci.lower ci.upper std.lv std.all std.nox
## 1 0.338 -0.024 0.071 0.023 0.045 0.045
## 2 0.010 -0.093 -0.013 -0.053 -0.098 -0.098
## 3 0.000 0.093 0.199 0.146 0.389 0.389
## 4 0.826 -0.063 0.051 -0.006 -0.013 -0.013
## 5 0.057 -0.002 0.136 0.067 0.139 0.139
## 6 0.000 0.090 0.184 0.137 0.260 0.260
## 7 0.523 -0.021 0.041 0.010 0.023 0.023
## 8 0.235 -0.027 0.112 0.042 0.069 0.069
## 9 0.044 0.000 0.010 0.005 0.055 0.009
## 10 0.022 0.008 0.106 0.057 0.113 0.099Models with significant interactions (Males)
Isolation x Community violence
model_obsMod1 <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age + Isolation_Loss:ChildComViolence
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + 0*ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ConflictTrauma ~~ ChildNeglectSexual
ChildAbuse ~~ ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ 0*ChildComViolence
ChildNeglectSexual ~~ Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs1 <- sem(model_obsMod1, data = datamale,estimator = "MLR", missing = "FIML.x", meanstructure = T, fixed.x = F)
datamale %>% select(Isolation_Loss,ChildComViolence, htq_ptsd_total) %>% report_table()## Variable | n_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | percentage_Missing
## -----------------------------------------------------------------------------------------------------------------
## Isolation_Loss | 642 | 0.45 | 0.97 | | 0.28 | -1.34 | 2.79 | 1.02 | -0.09 | 0.78
## ChildComViolence | 642 | -0.07 | 0.94 | | 1.31 | -2.87 | 1.39 | -0.06 | -0.92 | 2.80
## htq_ptsd_total | 642 | 2.10 | 0.52 | | 0.58 | 1.00 | 3.45 | 0.15 | -0.89 | 3.43result2way2 <- probe2WayMC(fit_obs1, nameX=c("Isolation_Loss","ChildComViolence","Isolation_Loss:ChildComViolence"),
nameY="htq_ptsd_total", modVar="ChildComViolence", valProbe = c(-0.5, 0, 1.4))
result2way2## $SimpleIntcept
## ChildComViolence est se z pvalue
## 1 -0.5 1.773 0.029 60.455 0
## 2 0.0 1.809 0.029 61.548 0
## 3 1.4 1.909 0.053 36.037 0
##
## $SimpleSlope
## ChildComViolence est se z pvalue
## 1 -0.5 -0.068 0.021 -3.245 0.001
## 2 0.0 -0.044 0.021 -2.127 0.033
## 3 1.4 0.022 0.038 0.584 0.559plotProbe(result2way2, xlim = c(-1.34, 2.79), xlab = "Isolation",
ylab = "PTSD Symptoms", legend = TRUE)
values_probe2 <- seq(from=-2.87, to = 1.39, by = 0.05)
result2way3 <- probe2WayMC(fit_obs1, nameX=c("Isolation_Loss","ChildComViolence","Isolation_Loss:ChildComViolence"),
nameY="htq_ptsd_total", modVar="ChildComViolence", valProbe=values_probe2)
result2way3## $SimpleIntcept
## ChildComViolence est se z pvalue
## 1 -2.87 1.603 0.076 21.074 0
## 2 -2.82 1.606 0.075 21.473 0
## 3 -2.77 1.610 0.074 21.885 0
## 4 -2.72 1.613 0.072 22.310 0
## 5 -2.67 1.617 0.071 22.748 0
## 6 -2.62 1.621 0.070 23.200 0
## 7 -2.57 1.624 0.069 23.668 0
## 8 -2.52 1.628 0.067 24.150 0
## 9 -2.47 1.631 0.066 24.649 0
## 10 -2.42 1.635 0.065 25.164 0
## 11 -2.37 1.639 0.064 25.697 0
## 12 -2.32 1.642 0.063 26.248 0
## 13 -2.27 1.646 0.061 26.818 0
## 14 -2.22 1.649 0.060 27.408 0
## 15 -2.17 1.653 0.059 28.018 0
## 16 -2.12 1.657 0.058 28.650 0
## 17 -2.07 1.660 0.057 29.303 0
## 18 -2.02 1.664 0.055 29.980 0
## 19 -1.97 1.667 0.054 30.681 0
## 20 -1.92 1.671 0.053 31.407 0
## 21 -1.87 1.674 0.052 32.159 0
## 22 -1.82 1.678 0.051 32.937 0
## 23 -1.77 1.682 0.050 33.742 0
## 24 -1.72 1.685 0.049 34.576 0
## 25 -1.67 1.689 0.048 35.439 0
## 26 -1.62 1.692 0.047 36.331 0
## 27 -1.57 1.696 0.046 37.254 0
## 28 -1.52 1.700 0.044 38.206 0
## 29 -1.47 1.703 0.043 39.189 0
## 30 -1.42 1.707 0.042 40.203 0
## 31 -1.37 1.710 0.041 41.246 0
## 32 -1.32 1.714 0.040 42.318 0
## 33 -1.27 1.717 0.040 43.419 0
## 34 -1.22 1.721 0.039 44.545 0
## 35 -1.17 1.725 0.038 45.695 0
## 36 -1.12 1.728 0.037 46.866 0
## 37 -1.07 1.732 0.036 48.054 0
## 38 -1.02 1.735 0.035 49.255 0
## 39 -0.97 1.739 0.034 50.461 0
## 40 -0.92 1.743 0.034 51.667 0
## 41 -0.87 1.746 0.033 52.865 0
## 42 -0.82 1.750 0.032 54.046 0
## 43 -0.77 1.753 0.032 55.200 0
## 44 -0.72 1.757 0.031 56.315 0
## 45 -0.67 1.760 0.031 57.380 0
## 46 -0.62 1.764 0.030 58.383 0
## 47 -0.57 1.768 0.030 59.309 0
## 48 -0.52 1.771 0.029 60.147 0
## 49 -0.47 1.775 0.029 60.884 0
## 50 -0.42 1.778 0.029 61.509 0
## 51 -0.37 1.782 0.029 62.011 0
## 52 -0.32 1.786 0.029 62.384 0
## 53 -0.27 1.789 0.029 62.620 0
## 54 -0.22 1.793 0.029 62.718 0
## 55 -0.17 1.796 0.029 62.676 0
## 56 -0.12 1.800 0.029 62.498 0
## 57 -0.07 1.804 0.029 62.189 0
## 58 -0.02 1.807 0.029 61.755 0
## 59 0.03 1.811 0.030 61.206 0
## 60 0.08 1.814 0.030 60.553 0
## 61 0.13 1.818 0.030 59.808 0
## 62 0.18 1.821 0.031 58.983 0
## 63 0.23 1.825 0.031 58.090 0
## 64 0.28 1.829 0.032 57.141 0
## 65 0.33 1.832 0.033 56.149 0
## 66 0.38 1.836 0.033 55.123 0
## 67 0.43 1.839 0.034 54.075 0
## 68 0.48 1.843 0.035 53.011 0
## 69 0.53 1.847 0.036 51.941 0
## 70 0.58 1.850 0.036 50.871 0
## 71 0.63 1.854 0.037 49.807 0
## 72 0.68 1.857 0.038 48.754 0
## 73 0.73 1.861 0.039 47.715 0
## 74 0.78 1.864 0.040 46.694 0
## 75 0.83 1.868 0.041 45.693 0
## 76 0.88 1.872 0.042 44.715 0
## 77 0.93 1.875 0.043 43.761 0
## 78 0.98 1.879 0.044 42.831 0
## 79 1.03 1.882 0.045 41.927 0
## 80 1.08 1.886 0.046 41.049 0
## 81 1.13 1.890 0.047 40.197 0
## 82 1.18 1.893 0.048 39.371 0
## 83 1.23 1.897 0.049 38.571 0
## 84 1.28 1.900 0.050 37.796 0
## 85 1.33 1.904 0.051 37.046 0
## 86 1.38 1.907 0.053 36.320 0
##
## $SimpleSlope
## ChildComViolence est se z pvalue
## 1 -2.87 -0.180 0.057 -3.172 0.002
## 2 -2.82 -0.177 0.056 -3.183 0.001
## 3 -2.77 -0.175 0.055 -3.194 0.001
## 4 -2.72 -0.173 0.054 -3.206 0.001
## 5 -2.67 -0.170 0.053 -3.217 0.001
## 6 -2.62 -0.168 0.052 -3.229 0.001
## 7 -2.57 -0.166 0.051 -3.241 0.001
## 8 -2.52 -0.163 0.050 -3.254 0.001
## 9 -2.47 -0.161 0.049 -3.266 0.001
## 10 -2.42 -0.158 0.048 -3.279 0.001
## 11 -2.37 -0.156 0.047 -3.292 0.001
## 12 -2.32 -0.154 0.047 -3.305 0.001
## 13 -2.27 -0.151 0.046 -3.319 0.001
## 14 -2.22 -0.149 0.045 -3.333 0.001
## 15 -2.17 -0.147 0.044 -3.346 0.001
## 16 -2.12 -0.144 0.043 -3.361 0.001
## 17 -2.07 -0.142 0.042 -3.375 0.001
## 18 -2.02 -0.139 0.041 -3.390 0.001
## 19 -1.97 -0.137 0.040 -3.404 0.001
## 20 -1.92 -0.135 0.039 -3.419 0.001
## 21 -1.87 -0.132 0.039 -3.434 0.001
## 22 -1.82 -0.130 0.038 -3.449 0.001
## 23 -1.77 -0.128 0.037 -3.464 0.001
## 24 -1.72 -0.125 0.036 -3.478 0.001
## 25 -1.67 -0.123 0.035 -3.493 0.000
## 26 -1.62 -0.121 0.034 -3.507 0.000
## 27 -1.57 -0.118 0.034 -3.521 0.000
## 28 -1.52 -0.116 0.033 -3.535 0.000
## 29 -1.47 -0.113 0.032 -3.548 0.000
## 30 -1.42 -0.111 0.031 -3.560 0.000
## 31 -1.37 -0.109 0.030 -3.571 0.000
## 32 -1.32 -0.106 0.030 -3.581 0.000
## 33 -1.27 -0.104 0.029 -3.590 0.000
## 34 -1.22 -0.102 0.028 -3.597 0.000
## 35 -1.17 -0.099 0.028 -3.602 0.000
## 36 -1.12 -0.097 0.027 -3.604 0.000
## 37 -1.07 -0.095 0.026 -3.604 0.000
## 38 -1.02 -0.092 0.026 -3.601 0.000
## 39 -0.97 -0.090 0.025 -3.594 0.000
## 40 -0.92 -0.087 0.024 -3.583 0.000
## 41 -0.87 -0.085 0.024 -3.567 0.000
## 42 -0.82 -0.083 0.023 -3.545 0.000
## 43 -0.77 -0.080 0.023 -3.518 0.000
## 44 -0.72 -0.078 0.022 -3.485 0.000
## 45 -0.67 -0.076 0.022 -3.444 0.001
## 46 -0.62 -0.073 0.022 -3.395 0.001
## 47 -0.57 -0.071 0.021 -3.339 0.001
## 48 -0.52 -0.068 0.021 -3.273 0.001
## 49 -0.47 -0.066 0.021 -3.200 0.001
## 50 -0.42 -0.064 0.020 -3.117 0.002
## 51 -0.37 -0.061 0.020 -3.025 0.002
## 52 -0.32 -0.059 0.020 -2.925 0.003
## 53 -0.27 -0.057 0.020 -2.817 0.005
## 54 -0.22 -0.054 0.020 -2.701 0.007
## 55 -0.17 -0.052 0.020 -2.579 0.010
## 56 -0.12 -0.050 0.020 -2.451 0.014
## 57 -0.07 -0.047 0.020 -2.318 0.020
## 58 -0.02 -0.045 0.021 -2.182 0.029
## 59 0.03 -0.042 0.021 -2.044 0.041
## 60 0.08 -0.040 0.021 -1.905 0.057
## 61 0.13 -0.038 0.021 -1.766 0.077
## 62 0.18 -0.035 0.022 -1.627 0.104
## 63 0.23 -0.033 0.022 -1.491 0.136
## 64 0.28 -0.031 0.023 -1.357 0.175
## 65 0.33 -0.028 0.023 -1.227 0.220
## 66 0.38 -0.026 0.024 -1.100 0.272
## 67 0.43 -0.024 0.024 -0.977 0.329
## 68 0.48 -0.021 0.025 -0.858 0.391
## 69 0.53 -0.019 0.025 -0.744 0.457
## 70 0.58 -0.016 0.026 -0.635 0.526
## 71 0.63 -0.014 0.026 -0.530 0.596
## 72 0.68 -0.012 0.027 -0.430 0.667
## 73 0.73 -0.009 0.028 -0.334 0.738
## 74 0.78 -0.007 0.029 -0.243 0.808
## 75 0.83 -0.005 0.029 -0.156 0.876
## 76 0.88 -0.002 0.030 -0.074 0.941
## 77 0.93 0.000 0.031 0.005 0.996
## 78 0.98 0.003 0.032 0.080 0.936
## 79 1.03 0.005 0.032 0.152 0.880
## 80 1.08 0.007 0.033 0.219 0.826
## 81 1.13 0.010 0.034 0.284 0.776
## 82 1.18 0.012 0.035 0.346 0.730
## 83 1.23 0.014 0.036 0.404 0.686
## 84 1.28 0.017 0.036 0.460 0.645
## 85 1.33 0.019 0.037 0.513 0.608
## 86 1.38 0.021 0.038 0.564 0.573# PRETTIER GRAPHS
fiti <- lm(htq_ptsd_total ~ ConflictTrauma + Isolation_Loss*ChildComViolence + ChildNeglectSexual + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + q102b_guess_age, data = datamale)
ss <- sim_slopes(fiti, pred = Isolation_Loss, modx = ChildComViolence, johnson_neyman = TRUE,control.fdr = TRUE)
ss ## JOHNSON-NEYMAN INTERVAL
##
## When ChildComViolence is OUTSIDE the interval [0.14, 25.58], the slope of
## Isolation_Loss is p < .05.
##
## Note: The range of observed values of ChildComViolence is [-2.87, 1.39]
##
## Interval calculated using false discovery rate adjusted t = 2.11
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of Isolation_Loss when ChildComViolence = -0.99103911 (- 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.09 0.03 -3.77 0.00
##
## Slope of Isolation_Loss when ChildComViolence = -0.05797478 (Mean):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.05 0.02 -2.67 0.01
##
## Slope of Isolation_Loss when ChildComViolence = 0.87508955 (+ 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.01 0.03 -0.40 0.69
probe_interaction(fiti, pred = Isolation_Loss, modx = ChildComViolence, cond.int = TRUE,
interval = TRUE, jnplot = TRUE)## JOHNSON-NEYMAN INTERVAL
##
## When ChildComViolence is OUTSIDE the interval [0.20, 11.53], the slope of
## Isolation_Loss is p < .05.
##
## Note: The range of observed values of ChildComViolence is [-2.87, 1.39]
## SIMPLE SLOPES ANALYSIS
##
## When ChildComViolence = -0.99103911 (- 1 SD):
##
## Est. S.E. t val. p
## ----------------------------- ------- ------ -------- ------
## Slope of Isolation_Loss -0.09 0.03 -3.77 0.00
## Conditional intercept 2.02 0.03 74.00 0.00
##
## When ChildComViolence = -0.05797478 (Mean):
##
## Est. S.E. t val. p
## ----------------------------- ------- ------ -------- ------
## Slope of Isolation_Loss -0.05 0.02 -2.67 0.01
## Conditional intercept 2.10 0.02 126.15 0.00
##
## When ChildComViolence = 0.87508955 (+ 1 SD):
##
## Est. S.E. t val. p
## ----------------------------- ------- ------ -------- ------
## Slope of Isolation_Loss -0.01 0.03 -0.40 0.69
## Conditional intercept 2.19 0.03 80.41 0.00
d <- interact_plot(fiti, pred = Isolation_Loss, modx = ChildComViolence, plot.points = TRUE, modx.values = "terciles", colors = "red", point.size = 1, point.alpha = 0.5, rug = T, jitter = 0.0, x.label = "Isolation & Loss", y.label = "PTSD symptoms", interval = T, legend.main = "Community violence", vary.lty = F)## Medians of each tercile of ChildComViolence are -0.9581, -0.0565, 1.0809
Witnessing violence x Community violence
Didn’t survive false discovery rate
model_obsMod1 <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age + WitnessViolence:ChildComViolence
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + 0*ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ConflictTrauma ~~ ChildNeglectSexual
ChildAbuse ~~ ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ 0*ChildComViolence
ChildNeglectSexual ~~ Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs1 <- sem(model_obsMod1, data = datamale,estimator = "MLR", missing = "FIML.x", meanstructure = T, fixed.x = F)
datamale %>% select(WitnessViolence,ChildComViolence) %>% report_table()## Variable | n_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | percentage_Missing
## -----------------------------------------------------------------------------------------------------------------
## WitnessViolence | 642 | 0.90 | 0.79 | | 0.61 | -1.61 | 2.64 | -0.82 | 1.16 | 0.78
## ChildComViolence | 642 | -0.07 | 0.94 | | 1.31 | -2.87 | 1.39 | -0.06 | -0.92 | 2.80result2way2 <- probe2WayMC(fit_obs1, nameX=c("WitnessViolence","ChildComViolence","WitnessViolence:ChildComViolence"),
nameY="htq_ptsd_total", modVar="ChildComViolence", valProbe = c(-1, 0, 0.8))
result2way2## $SimpleIntcept
## ChildComViolence est se z pvalue
## 1 -1.0 1.757 0.037 47.114 0
## 2 0.0 1.800 0.031 57.819 0
## 3 0.8 1.833 0.049 37.071 0
##
## $SimpleSlope
## ChildComViolence est se z pvalue
## 1 -1.0 0.010 0.040 0.244 0.807
## 2 0.0 0.067 0.035 1.902 0.057
## 3 0.8 0.113 0.043 2.641 0.008plotProbe(result2way2, xlim = c(-1.61, 2.64), xlab = "WitnessViolence",
ylab = "PTSD Symptoms", legend = TRUE)
values_probe2 <- seq(from=-2.87, to = 1.39, by = 0.05)
result2way3 <- probe2WayMC(fit_obs1, nameX=c("WitnessViolence","ChildComViolence","WitnessViolence:ChildComViolence"),
nameY="htq_ptsd_total", modVar="ChildComViolence", valProbe=values_probe2)
result2way3## $SimpleIntcept
## ChildComViolence est se z pvalue
## 1 -2.87 1.678 0.095 17.745 0
## 2 -2.82 1.681 0.093 18.091 0
## 3 -2.77 1.683 0.091 18.448 0
## 4 -2.72 1.685 0.090 18.819 0
## 5 -2.67 1.687 0.088 19.202 0
## 6 -2.62 1.689 0.086 19.600 0
## 7 -2.57 1.691 0.085 20.013 0
## 8 -2.52 1.693 0.083 20.441 0
## 9 -2.47 1.695 0.081 20.885 0
## 10 -2.42 1.697 0.080 21.347 0
## 11 -2.37 1.700 0.078 21.827 0
## 12 -2.32 1.702 0.076 22.326 0
## 13 -2.27 1.704 0.075 22.846 0
## 14 -2.22 1.706 0.073 23.386 0
## 15 -2.17 1.708 0.071 23.950 0
## 16 -2.12 1.710 0.070 24.537 0
## 17 -2.07 1.712 0.068 25.149 0
## 18 -2.02 1.714 0.066 25.787 0
## 19 -1.97 1.716 0.065 26.454 0
## 20 -1.92 1.718 0.063 27.150 0
## 21 -1.87 1.721 0.062 27.877 0
## 22 -1.82 1.723 0.060 28.637 0
## 23 -1.77 1.725 0.059 29.432 0
## 24 -1.72 1.727 0.057 30.263 0
## 25 -1.67 1.729 0.056 31.132 0
## 26 -1.62 1.731 0.054 32.041 0
## 27 -1.57 1.733 0.053 32.992 0
## 28 -1.52 1.735 0.051 33.986 0
## 29 -1.47 1.737 0.050 35.026 0
## 30 -1.42 1.740 0.048 36.113 0
## 31 -1.37 1.742 0.047 37.248 0
## 32 -1.32 1.744 0.045 38.431 0
## 33 -1.27 1.746 0.044 39.664 0
## 34 -1.22 1.748 0.043 40.946 0
## 35 -1.17 1.750 0.041 42.276 0
## 36 -1.12 1.752 0.040 43.652 0
## 37 -1.07 1.754 0.039 45.069 0
## 38 -1.02 1.756 0.038 46.524 0
## 39 -0.97 1.759 0.037 48.007 0
## 40 -0.92 1.761 0.036 49.510 0
## 41 -0.87 1.763 0.035 51.021 0
## 42 -0.82 1.765 0.034 52.523 0
## 43 -0.77 1.767 0.033 53.999 0
## 44 -0.72 1.769 0.032 55.426 0
## 45 -0.67 1.771 0.031 56.780 0
## 46 -0.62 1.773 0.031 58.035 0
## 47 -0.57 1.775 0.030 59.162 0
## 48 -0.52 1.778 0.030 60.134 0
## 49 -0.47 1.780 0.029 60.924 0
## 50 -0.42 1.782 0.029 61.510 0
## 51 -0.37 1.784 0.029 61.875 0
## 52 -0.32 1.786 0.029 62.007 0
## 53 -0.27 1.788 0.029 61.905 0
## 54 -0.22 1.790 0.029 61.573 0
## 55 -0.17 1.792 0.029 61.024 0
## 56 -0.12 1.794 0.030 60.276 0
## 57 -0.07 1.797 0.030 59.354 0
## 58 -0.02 1.799 0.031 58.282 0
## 59 0.03 1.801 0.032 57.090 0
## 60 0.08 1.803 0.032 55.804 0
## 61 0.13 1.805 0.033 54.450 0
## 62 0.18 1.807 0.034 53.051 0
## 63 0.23 1.809 0.035 51.628 0
## 64 0.28 1.811 0.036 50.197 0
## 65 0.33 1.813 0.037 48.773 0
## 66 0.38 1.816 0.038 47.368 0
## 67 0.43 1.818 0.040 45.991 0
## 68 0.48 1.820 0.041 44.648 0
## 69 0.53 1.822 0.042 43.344 0
## 70 0.58 1.824 0.043 42.082 0
## 71 0.63 1.826 0.045 40.866 0
## 72 0.68 1.828 0.046 39.695 0
## 73 0.73 1.830 0.047 38.569 0
## 74 0.78 1.832 0.049 37.490 0
## 75 0.83 1.835 0.050 36.455 0
## 76 0.88 1.837 0.052 35.464 0
## 77 0.93 1.839 0.053 34.516 0
## 78 0.98 1.841 0.055 33.608 0
## 79 1.03 1.843 0.056 32.739 0
## 80 1.08 1.845 0.058 31.908 0
## 81 1.13 1.847 0.059 31.113 0
## 82 1.18 1.849 0.061 30.352 0
## 83 1.23 1.851 0.062 29.623 0
## 84 1.28 1.854 0.064 28.925 0
## 85 1.33 1.856 0.066 28.257 0
## 86 1.38 1.858 0.067 27.616 0
##
## $SimpleSlope
## ChildComViolence est se z pvalue
## 1 -2.87 -0.097 0.076 -1.280 0.200
## 2 -2.82 -0.094 0.075 -1.261 0.207
## 3 -2.77 -0.091 0.073 -1.241 0.215
## 4 -2.72 -0.088 0.072 -1.221 0.222
## 5 -2.67 -0.085 0.071 -1.199 0.230
## 6 -2.62 -0.083 0.070 -1.177 0.239
## 7 -2.57 -0.080 0.069 -1.154 0.248
## 8 -2.52 -0.077 0.068 -1.130 0.258
## 9 -2.47 -0.074 0.067 -1.106 0.269
## 10 -2.42 -0.071 0.066 -1.080 0.280
## 11 -2.37 -0.068 0.065 -1.054 0.292
## 12 -2.32 -0.065 0.064 -1.026 0.305
## 13 -2.27 -0.063 0.063 -0.998 0.318
## 14 -2.22 -0.060 0.062 -0.968 0.333
## 15 -2.17 -0.057 0.061 -0.938 0.348
## 16 -2.12 -0.054 0.060 -0.906 0.365
## 17 -2.07 -0.051 0.059 -0.873 0.383
## 18 -2.02 -0.048 0.058 -0.838 0.402
## 19 -1.97 -0.045 0.057 -0.803 0.422
## 20 -1.92 -0.043 0.056 -0.765 0.444
## 21 -1.87 -0.040 0.055 -0.727 0.467
## 22 -1.82 -0.037 0.054 -0.687 0.492
## 23 -1.77 -0.034 0.053 -0.645 0.519
## 24 -1.72 -0.031 0.052 -0.601 0.548
## 25 -1.67 -0.028 0.051 -0.556 0.578
## 26 -1.62 -0.026 0.050 -0.509 0.611
## 27 -1.57 -0.023 0.049 -0.460 0.645
## 28 -1.52 -0.020 0.048 -0.410 0.682
## 29 -1.47 -0.017 0.047 -0.357 0.721
## 30 -1.42 -0.014 0.047 -0.302 0.763
## 31 -1.37 -0.011 0.046 -0.245 0.806
## 32 -1.32 -0.008 0.045 -0.186 0.852
## 33 -1.27 -0.006 0.044 -0.125 0.901
## 34 -1.22 -0.003 0.043 -0.062 0.951
## 35 -1.17 0.000 0.043 0.004 0.997
## 36 -1.12 0.003 0.042 0.072 0.943
## 37 -1.07 0.006 0.041 0.142 0.887
## 38 -1.02 0.009 0.041 0.215 0.830
## 39 -0.97 0.012 0.040 0.289 0.772
## 40 -0.92 0.014 0.039 0.366 0.715
## 41 -0.87 0.017 0.039 0.444 0.657
## 42 -0.82 0.020 0.038 0.525 0.600
## 43 -0.77 0.023 0.038 0.607 0.544
## 44 -0.72 0.026 0.037 0.691 0.490
## 45 -0.67 0.029 0.037 0.776 0.438
## 46 -0.62 0.032 0.037 0.863 0.388
## 47 -0.57 0.034 0.036 0.950 0.342
## 48 -0.52 0.037 0.036 1.037 0.300
## 49 -0.47 0.040 0.036 1.125 0.260
## 50 -0.42 0.043 0.035 1.213 0.225
## 51 -0.37 0.046 0.035 1.301 0.193
## 52 -0.32 0.049 0.035 1.388 0.165
## 53 -0.27 0.052 0.035 1.473 0.141
## 54 -0.22 0.054 0.035 1.557 0.119
## 55 -0.17 0.057 0.035 1.640 0.101
## 56 -0.12 0.060 0.035 1.720 0.085
## 57 -0.07 0.063 0.035 1.797 0.072
## 58 -0.02 0.066 0.035 1.872 0.061
## 59 0.03 0.069 0.035 1.945 0.052
## 60 0.08 0.072 0.036 2.014 0.044
## 61 0.13 0.074 0.036 2.079 0.038
## 62 0.18 0.077 0.036 2.142 0.032
## 63 0.23 0.080 0.036 2.200 0.028
## 64 0.28 0.083 0.037 2.256 0.024
## 65 0.33 0.086 0.037 2.308 0.021
## 66 0.38 0.089 0.038 2.356 0.018
## 67 0.43 0.091 0.038 2.401 0.016
## 68 0.48 0.094 0.039 2.443 0.015
## 69 0.53 0.097 0.039 2.482 0.013
## 70 0.58 0.100 0.040 2.517 0.012
## 71 0.63 0.103 0.040 2.550 0.011
## 72 0.68 0.106 0.041 2.580 0.010
## 73 0.73 0.109 0.042 2.607 0.009
## 74 0.78 0.111 0.042 2.632 0.008
## 75 0.83 0.114 0.043 2.654 0.008
## 76 0.88 0.117 0.044 2.674 0.007
## 77 0.93 0.120 0.045 2.692 0.007
## 78 0.98 0.123 0.045 2.709 0.007
## 79 1.03 0.126 0.046 2.723 0.006
## 80 1.08 0.129 0.047 2.736 0.006
## 81 1.13 0.131 0.048 2.747 0.006
## 82 1.18 0.134 0.049 2.757 0.006
## 83 1.23 0.137 0.050 2.766 0.006
## 84 1.28 0.140 0.050 2.774 0.006
## 85 1.33 0.143 0.051 2.780 0.005
## 86 1.38 0.146 0.052 2.786 0.005# PRETTIER GRAPHS
fiti <- lm(htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + WitnessViolence*ChildComViolence + ChildNeglectSexual + ViolentVictimization + Destruction_Injury + ChildAbuse + q102b_guess_age, data = datamale)
ss <- sim_slopes(fiti, pred = WitnessViolence, modx = ChildComViolence, johnson_neyman = TRUE,control.fdr = TRUE)
ss ## JOHNSON-NEYMAN INTERVAL
##
## The Johnson-Neyman interval could not be found. Is the p value for your
## interaction term below the specified alpha?
##
## Interval calculated using false discovery rate adjusted t = 4.09
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of WitnessViolence when ChildComViolence = -0.99103911 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.01 0.04 0.30 0.76
##
## Slope of WitnessViolence when ChildComViolence = -0.05797478 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.06 0.04 1.76 0.08
##
## Slope of WitnessViolence when ChildComViolence = 0.87508955 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.11 0.05 2.53 0.01Migration violence x abuse
Implausible values
model_obsMod1 <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age + Destruction_Injury:ChildAbuse
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + 0*ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ConflictTrauma ~~ ChildNeglectSexual
ChildAbuse ~~ ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ 0*ChildComViolence
ChildNeglectSexual ~~ Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs1 <- sem(model_obsMod1, data = datamale,estimator = "MLR", missing = "FIML.x", meanstructure = T, fixed.x = F)
datamale %>% select(Destruction_Injury,ChildAbuse) %>% report_table()## Variable | n_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | percentage_Missing
## ------------------------------------------------------------------------------------------------------------------
## Destruction_Injury | 642 | 0.69 | 0.93 | | 0.94 | -1.94 | 2.74 | -0.26 | -0.12 | 0.78
## ChildAbuse | 642 | 0.47 | 0.99 | | 0.92 | -1.95 | 2.61 | 0.45 | -0.41 | 2.80result2way2 <- probe2WayMC(fit_obs1, nameX=c("Destruction_Injury","ChildAbuse","Destruction_Injury:ChildAbuse"),
nameY="htq_ptsd_total", modVar="ChildAbuse", valProbe = c(-0.5, 0, 1.4))
result2way2## $SimpleIntcept
## ChildAbuse est se z pvalue
## 1 -0.5 1.756 0.032 54.249 0
## 2 0.0 1.817 0.029 61.602 0
## 3 1.4 1.988 0.050 40.060 0
##
## $SimpleSlope
## ChildAbuse est se z pvalue
## 1 -0.5 -0.038 0.032 -1.179 0.238
## 2 0.0 -0.021 0.030 -0.722 0.471
## 3 1.4 0.026 0.034 0.749 0.454plotProbe(result2way2, xlim = c(-1.94, 2.74), xlab = "Migration violence",
ylab = "PTSD Symptoms", legend = TRUE)
values_probe2 <- seq(from=-1.95, to = 2.61, by = 0.05)
result2way3 <- probe2WayMC(fit_obs1, nameX=c("Destruction_Injury","ChildAbuse","Destruction_Injury:ChildAbuse"),
nameY="htq_ptsd_total", modVar="ChildAbuse", valProbe=values_probe2)
result2way3## $SimpleIntcept
## ChildAbuse est se z pvalue
## 1 -1.95 1.578 0.062 25.660 0
## 2 -1.90 1.584 0.060 26.283 0
## 3 -1.85 1.591 0.059 26.928 0
## 4 -1.80 1.597 0.058 27.597 0
## 5 -1.75 1.603 0.057 28.290 0
## 6 -1.70 1.609 0.055 29.009 0
## 7 -1.65 1.615 0.054 29.753 0
## 8 -1.60 1.621 0.053 30.524 0
## 9 -1.55 1.627 0.052 31.323 0
## 10 -1.50 1.633 0.051 32.151 0
## 11 -1.45 1.639 0.050 33.009 0
## 12 -1.40 1.646 0.049 33.896 0
## 13 -1.35 1.652 0.047 34.815 0
## 14 -1.30 1.658 0.046 35.766 0
## 15 -1.25 1.664 0.045 36.748 0
## 16 -1.20 1.670 0.044 37.762 0
## 17 -1.15 1.676 0.043 38.808 0
## 18 -1.10 1.682 0.042 39.885 0
## 19 -1.05 1.688 0.041 40.993 0
## 20 -1.00 1.694 0.040 42.130 0
## 21 -0.95 1.701 0.039 43.293 0
## 22 -0.90 1.707 0.038 44.481 0
## 23 -0.85 1.713 0.037 45.690 0
## 24 -0.80 1.719 0.037 46.915 0
## 25 -0.75 1.725 0.036 48.152 0
## 26 -0.70 1.731 0.035 49.394 0
## 27 -0.65 1.737 0.034 50.634 0
## 28 -0.60 1.743 0.034 51.863 0
## 29 -0.55 1.749 0.033 53.072 0
## 30 -0.50 1.756 0.032 54.249 0
## 31 -0.45 1.762 0.032 55.382 0
## 32 -0.40 1.768 0.031 56.460 0
## 33 -0.35 1.774 0.031 57.470 0
## 34 -0.30 1.780 0.030 58.397 0
## 35 -0.25 1.786 0.030 59.230 0
## 36 -0.20 1.792 0.030 59.955 0
## 37 -0.15 1.798 0.030 60.563 0
## 38 -0.10 1.804 0.030 61.044 0
## 39 -0.05 1.811 0.029 61.392 0
## 40 0.00 1.817 0.029 61.602 0
## 41 0.05 1.823 0.030 61.673 0
## 42 0.10 1.829 0.030 61.606 0
## 43 0.15 1.835 0.030 61.406 0
## 44 0.20 1.841 0.030 61.078 0
## 45 0.25 1.847 0.030 60.631 0
## 46 0.30 1.853 0.031 60.077 0
## 47 0.35 1.859 0.031 59.425 0
## 48 0.40 1.866 0.032 58.688 0
## 49 0.45 1.872 0.032 57.879 0
## 50 0.50 1.878 0.033 57.009 0
## 51 0.55 1.884 0.034 56.091 0
## 52 0.60 1.890 0.034 55.135 0
## 53 0.65 1.896 0.035 54.151 0
## 54 0.70 1.902 0.036 53.148 0
## 55 0.75 1.908 0.037 52.135 0
## 56 0.80 1.914 0.037 51.118 0
## 57 0.85 1.921 0.038 50.104 0
## 58 0.90 1.927 0.039 49.097 0
## 59 0.95 1.933 0.040 48.102 0
## 60 1.00 1.939 0.041 47.123 0
## 61 1.05 1.945 0.042 46.161 0
## 62 1.10 1.951 0.043 45.220 0
## 63 1.15 1.957 0.044 44.300 0
## 64 1.20 1.963 0.045 43.403 0
## 65 1.25 1.969 0.046 42.531 0
## 66 1.30 1.976 0.047 41.682 0
## 67 1.35 1.982 0.049 40.859 0
## 68 1.40 1.988 0.050 40.060 0
## 69 1.45 1.994 0.051 39.285 0
## 70 1.50 2.000 0.052 38.534 0
## 71 1.55 2.006 0.053 37.808 0
## 72 1.60 2.012 0.054 37.104 0
## 73 1.65 2.018 0.055 36.423 0
## 74 1.70 2.024 0.057 35.764 0
## 75 1.75 2.031 0.058 35.127 0
## 76 1.80 2.037 0.059 34.511 0
## 77 1.85 2.043 0.060 33.914 0
## 78 1.90 2.049 0.061 33.337 0
## 79 1.95 2.055 0.063 32.779 0
## 80 2.00 2.061 0.064 32.239 0
## 81 2.05 2.067 0.065 31.716 0
## 82 2.10 2.073 0.066 31.210 0
## 83 2.15 2.079 0.068 30.720 0
## 84 2.20 2.086 0.069 30.245 0
## 85 2.25 2.092 0.070 29.785 0
## 86 2.30 2.098 0.072 29.340 0
## 87 2.35 2.104 0.073 28.908 0
## 88 2.40 2.110 0.074 28.490 0
## 89 2.45 2.116 0.075 28.084 0
## 90 2.50 2.122 0.077 27.690 0
## 91 2.55 2.128 0.078 27.309 0
## 92 2.60 2.134 0.079 26.938 0
##
## $SimpleSlope
## ChildAbuse est se z pvalue
## 1 -1.95 -0.087 0.049 -1.787 0.074
## 2 -1.90 -0.085 0.048 -1.778 0.075
## 3 -1.85 -0.083 0.047 -1.767 0.077
## 4 -1.80 -0.082 0.047 -1.757 0.079
## 5 -1.75 -0.080 0.046 -1.746 0.081
## 6 -1.70 -0.078 0.045 -1.734 0.083
## 7 -1.65 -0.077 0.045 -1.721 0.085
## 8 -1.60 -0.075 0.044 -1.708 0.088
## 9 -1.55 -0.073 0.043 -1.695 0.090
## 10 -1.50 -0.072 0.043 -1.680 0.093
## 11 -1.45 -0.070 0.042 -1.665 0.096
## 12 -1.40 -0.068 0.041 -1.649 0.099
## 13 -1.35 -0.067 0.041 -1.633 0.103
## 14 -1.30 -0.065 0.040 -1.615 0.106
## 15 -1.25 -0.063 0.040 -1.597 0.110
## 16 -1.20 -0.062 0.039 -1.577 0.115
## 17 -1.15 -0.060 0.039 -1.557 0.120
## 18 -1.10 -0.058 0.038 -1.535 0.125
## 19 -1.05 -0.057 0.037 -1.513 0.130
## 20 -1.00 -0.055 0.037 -1.489 0.136
## 21 -0.95 -0.053 0.036 -1.464 0.143
## 22 -0.90 -0.052 0.036 -1.438 0.150
## 23 -0.85 -0.050 0.035 -1.411 0.158
## 24 -0.80 -0.048 0.035 -1.382 0.167
## 25 -0.75 -0.047 0.034 -1.352 0.176
## 26 -0.70 -0.045 0.034 -1.320 0.187
## 27 -0.65 -0.043 0.034 -1.287 0.198
## 28 -0.60 -0.042 0.033 -1.253 0.210
## 29 -0.55 -0.040 0.033 -1.217 0.224
## 30 -0.50 -0.038 0.032 -1.179 0.238
## 31 -0.45 -0.036 0.032 -1.140 0.254
## 32 -0.40 -0.035 0.032 -1.100 0.271
## 33 -0.35 -0.033 0.031 -1.058 0.290
## 34 -0.30 -0.031 0.031 -1.014 0.311
## 35 -0.25 -0.030 0.031 -0.969 0.333
## 36 -0.20 -0.028 0.030 -0.922 0.356
## 37 -0.15 -0.026 0.030 -0.874 0.382
## 38 -0.10 -0.025 0.030 -0.825 0.410
## 39 -0.05 -0.023 0.030 -0.774 0.439
## 40 0.00 -0.021 0.030 -0.722 0.471
## 41 0.05 -0.020 0.029 -0.668 0.504
## 42 0.10 -0.018 0.029 -0.614 0.539
## 43 0.15 -0.016 0.029 -0.559 0.576
## 44 0.20 -0.015 0.029 -0.503 0.615
## 45 0.25 -0.013 0.029 -0.446 0.656
## 46 0.30 -0.011 0.029 -0.389 0.698
## 47 0.35 -0.010 0.029 -0.331 0.741
## 48 0.40 -0.008 0.029 -0.273 0.785
## 49 0.45 -0.006 0.029 -0.215 0.830
## 50 0.50 -0.005 0.029 -0.157 0.876
## 51 0.55 -0.003 0.029 -0.099 0.921
## 52 0.60 -0.001 0.029 -0.041 0.967
## 53 0.65 0.000 0.030 0.016 0.987
## 54 0.70 0.002 0.030 0.072 0.942
## 55 0.75 0.004 0.030 0.128 0.898
## 56 0.80 0.006 0.030 0.183 0.855
## 57 0.85 0.007 0.030 0.237 0.813
## 58 0.90 0.009 0.031 0.290 0.772
## 59 0.95 0.011 0.031 0.341 0.733
## 60 1.00 0.012 0.031 0.392 0.695
## 61 1.05 0.014 0.031 0.441 0.659
## 62 1.10 0.016 0.032 0.489 0.625
## 63 1.15 0.017 0.032 0.536 0.592
## 64 1.20 0.019 0.033 0.581 0.561
## 65 1.25 0.021 0.033 0.625 0.532
## 66 1.30 0.022 0.033 0.668 0.504
## 67 1.35 0.024 0.034 0.709 0.478
## 68 1.40 0.026 0.034 0.749 0.454
## 69 1.45 0.027 0.035 0.787 0.431
## 70 1.50 0.029 0.035 0.825 0.410
## 71 1.55 0.031 0.036 0.860 0.390
## 72 1.60 0.032 0.036 0.895 0.371
## 73 1.65 0.034 0.037 0.928 0.353
## 74 1.70 0.036 0.037 0.960 0.337
## 75 1.75 0.037 0.038 0.991 0.322
## 76 1.80 0.039 0.038 1.020 0.308
## 77 1.85 0.041 0.039 1.049 0.294
## 78 1.90 0.042 0.039 1.076 0.282
## 79 1.95 0.044 0.040 1.103 0.270
## 80 2.00 0.046 0.041 1.128 0.259
## 81 2.05 0.047 0.041 1.152 0.249
## 82 2.10 0.049 0.042 1.176 0.240
## 83 2.15 0.051 0.042 1.198 0.231
## 84 2.20 0.053 0.043 1.220 0.223
## 85 2.25 0.054 0.044 1.240 0.215
## 86 2.30 0.056 0.044 1.260 0.208
## 87 2.35 0.058 0.045 1.279 0.201
## 88 2.40 0.059 0.046 1.298 0.194
## 89 2.45 0.061 0.046 1.316 0.188
## 90 2.50 0.063 0.047 1.333 0.183
## 91 2.55 0.064 0.048 1.349 0.177
## 92 2.60 0.066 0.048 1.365 0.172# PRETTIER GRAPHS
fiti <- lm(htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ChildComViolence + ChildNeglectSexual + ViolentVictimization + Destruction_Injury*ChildAbuse + WitnessViolence + q102b_guess_age, data = datamale)
ss <- sim_slopes(fiti, pred = Destruction_Injury, modx = ChildAbuse, johnson_neyman = TRUE,control.fdr = TRUE)
ss ## JOHNSON-NEYMAN INTERVAL
##
## The Johnson-Neyman interval could not be found. Is the p value for your
## interaction term below the specified alpha?
##
## Interval calculated using false discovery rate adjusted t = 4.09
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of Destruction_Injury when ChildAbuse = -0.5069424 (- 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.04 0.04 -1.19 0.24
##
## Slope of Destruction_Injury when ChildAbuse = 0.4819704 (Mean):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.01 0.03 -0.30 0.77
##
## Slope of Destruction_Injury when ChildAbuse = 1.4708832 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.02 0.04 0.64 0.52Witness x Abuse
model_obsMod1 <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age + WitnessViolence:ChildAbuse
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + 0*ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ConflictTrauma ~~ ChildNeglectSexual
ChildAbuse ~~ ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ 0*ChildComViolence
ChildNeglectSexual ~~ Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs1 <- sem(model_obsMod1, data = datamale,estimator = "MLR", missing = "FIML.x", meanstructure = T, fixed.x = F)
datamale %>% select(WitnessViolence,ChildAbuse) %>% report_table()## Variable | n_Obs | Mean | SD | Median | MAD | Min | Max | Skewness | Kurtosis | percentage_Missing
## ---------------------------------------------------------------------------------------------------------------
## WitnessViolence | 642 | 0.90 | 0.79 | | 0.61 | -1.61 | 2.64 | -0.82 | 1.16 | 0.78
## ChildAbuse | 642 | 0.47 | 0.99 | | 0.92 | -1.95 | 2.61 | 0.45 | -0.41 | 2.80result2way2 <- probe2WayMC(fit_obs1, nameX=c("WitnessViolence","ChildAbuse","WitnessViolence:ChildAbuse"),
nameY="htq_ptsd_total", modVar="ChildAbuse", valProbe = c(-0.5, 0, 1.4))
result2way2## $SimpleIntcept
## ChildAbuse est se z pvalue
## 1 -0.5 1.771 0.033 53.291 0
## 2 0.0 1.817 0.029 61.740 0
## 3 1.4 1.945 0.065 30.147 0
##
## $SimpleSlope
## ChildAbuse est se z pvalue
## 1 -0.5 0.017 0.038 0.455 0.649
## 2 0.0 0.044 0.035 1.245 0.213
## 3 1.4 0.117 0.047 2.480 0.013plotProbe(result2way2, xlim = c(-1.61, 2.64), xlab = "Migration violence",
ylab = "PTSD Symptoms", legend = TRUE)
values_probe2 <- seq(from=-1.95, to = 2.61, by = 0.05)
result2way3 <- probe2WayMC(fit_obs1, nameX=c("WitnessViolence","ChildAbuse","WitnessViolence:ChildAbuse"),
nameY="htq_ptsd_total", modVar="ChildAbuse", valProbe=values_probe2)
result2way3## $SimpleIntcept
## ChildAbuse est se z pvalue
## 1 -1.95 1.638 0.078 21.128 0
## 2 -1.90 1.642 0.076 21.687 0
## 3 -1.85 1.647 0.074 22.270 0
## 4 -1.80 1.652 0.072 22.879 0
## 5 -1.75 1.656 0.070 23.516 0
## 6 -1.70 1.661 0.069 24.183 0
## 7 -1.65 1.665 0.067 24.880 0
## 8 -1.60 1.670 0.065 25.611 0
## 9 -1.55 1.675 0.063 26.377 0
## 10 -1.50 1.679 0.062 27.180 0
## 11 -1.45 1.684 0.060 28.023 0
## 12 -1.40 1.688 0.058 28.907 0
## 13 -1.35 1.693 0.057 29.835 0
## 14 -1.30 1.697 0.055 30.810 0
## 15 -1.25 1.702 0.053 31.833 0
## 16 -1.20 1.707 0.052 32.907 0
## 17 -1.15 1.711 0.050 34.034 0
## 18 -1.10 1.716 0.049 35.216 0
## 19 -1.05 1.720 0.047 36.455 0
## 20 -1.00 1.725 0.046 37.753 0
## 21 -0.95 1.730 0.044 39.108 0
## 22 -0.90 1.734 0.043 40.522 0
## 23 -0.85 1.739 0.041 41.993 0
## 24 -0.80 1.743 0.040 43.516 0
## 25 -0.75 1.748 0.039 45.088 0
## 26 -0.70 1.752 0.038 46.701 0
## 27 -0.65 1.757 0.036 48.343 0
## 28 -0.60 1.762 0.035 50.002 0
## 29 -0.55 1.766 0.034 51.658 0
## 30 -0.50 1.771 0.033 53.291 0
## 31 -0.45 1.775 0.032 54.873 0
## 32 -0.40 1.780 0.032 56.376 0
## 33 -0.35 1.785 0.031 57.765 0
## 34 -0.30 1.789 0.030 59.006 0
## 35 -0.25 1.794 0.030 60.064 0
## 36 -0.20 1.798 0.030 60.908 0
## 37 -0.15 1.803 0.029 61.512 0
## 38 -0.10 1.807 0.029 61.856 0
## 39 -0.05 1.812 0.029 61.932 0
## 40 0.00 1.817 0.029 61.740 0
## 41 0.05 1.821 0.030 61.292 0
## 42 0.10 1.826 0.030 60.608 0
## 43 0.15 1.830 0.031 59.715 0
## 44 0.20 1.835 0.031 58.644 0
## 45 0.25 1.840 0.032 57.430 0
## 46 0.30 1.844 0.033 56.105 0
## 47 0.35 1.849 0.034 54.700 0
## 48 0.40 1.853 0.035 53.244 0
## 49 0.45 1.858 0.036 51.762 0
## 50 0.50 1.862 0.037 50.272 0
## 51 0.55 1.867 0.038 48.793 0
## 52 0.60 1.872 0.040 47.337 0
## 53 0.65 1.876 0.041 45.914 0
## 54 0.70 1.881 0.042 44.531 0
## 55 0.75 1.885 0.044 43.193 0
## 56 0.80 1.890 0.045 41.904 0
## 57 0.85 1.895 0.047 40.665 0
## 58 0.90 1.899 0.048 39.476 0
## 59 0.95 1.904 0.050 38.338 0
## 60 1.00 1.908 0.051 37.250 0
## 61 1.05 1.913 0.053 36.211 0
## 62 1.10 1.917 0.054 35.219 0
## 63 1.15 1.922 0.056 34.272 0
## 64 1.20 1.927 0.058 33.368 0
## 65 1.25 1.931 0.059 32.506 0
## 66 1.30 1.936 0.061 31.683 0
## 67 1.35 1.940 0.063 30.897 0
## 68 1.40 1.945 0.065 30.147 0
## 69 1.45 1.950 0.066 29.430 0
## 70 1.50 1.954 0.068 28.745 0
## 71 1.55 1.959 0.070 28.091 0
## 72 1.60 1.963 0.071 27.464 0
## 73 1.65 1.968 0.073 26.865 0
## 74 1.70 1.972 0.075 26.291 0
## 75 1.75 1.977 0.077 25.740 0
## 76 1.80 1.982 0.079 25.213 0
## 77 1.85 1.986 0.080 24.707 0
## 78 1.90 1.991 0.082 24.221 0
## 79 1.95 1.995 0.084 23.755 0
## 80 2.00 2.000 0.086 23.307 0
## 81 2.05 2.005 0.088 22.876 0
## 82 2.10 2.009 0.089 22.461 0
## 83 2.15 2.014 0.091 22.062 0
## 84 2.20 2.018 0.093 21.677 0
## 85 2.25 2.023 0.095 21.307 0
## 86 2.30 2.028 0.097 20.949 0
## 87 2.35 2.032 0.099 20.604 0
## 88 2.40 2.037 0.100 20.272 0
## 89 2.45 2.041 0.102 19.950 0
## 90 2.50 2.046 0.104 19.640 0
## 91 2.55 2.050 0.106 19.339 0
## 92 2.60 2.055 0.108 19.049 0
##
## $SimpleSlope
## ChildAbuse est se z pvalue
## 1 -1.95 -0.058 0.063 -0.935 0.350
## 2 -1.90 -0.056 0.061 -0.908 0.364
## 3 -1.85 -0.053 0.060 -0.880 0.379
## 4 -1.80 -0.051 0.059 -0.851 0.395
## 5 -1.75 -0.048 0.058 -0.821 0.412
## 6 -1.70 -0.045 0.057 -0.790 0.430
## 7 -1.65 -0.043 0.056 -0.757 0.449
## 8 -1.60 -0.040 0.055 -0.723 0.470
## 9 -1.55 -0.038 0.055 -0.688 0.491
## 10 -1.50 -0.035 0.054 -0.651 0.515
## 11 -1.45 -0.032 0.053 -0.613 0.540
## 12 -1.40 -0.030 0.052 -0.574 0.566
## 13 -1.35 -0.027 0.051 -0.533 0.594
## 14 -1.30 -0.024 0.050 -0.490 0.624
## 15 -1.25 -0.022 0.049 -0.445 0.656
## 16 -1.20 -0.019 0.048 -0.399 0.690
## 17 -1.15 -0.017 0.047 -0.351 0.726
## 18 -1.10 -0.014 0.046 -0.301 0.763
## 19 -1.05 -0.011 0.046 -0.249 0.803
## 20 -1.00 -0.009 0.045 -0.195 0.845
## 21 -0.95 -0.006 0.044 -0.139 0.889
## 22 -0.90 -0.004 0.043 -0.081 0.935
## 23 -0.85 -0.001 0.043 -0.021 0.983
## 24 -0.80 0.002 0.042 0.041 0.967
## 25 -0.75 0.004 0.041 0.105 0.916
## 26 -0.70 0.007 0.041 0.171 0.864
## 27 -0.65 0.010 0.040 0.239 0.811
## 28 -0.60 0.012 0.039 0.310 0.757
## 29 -0.55 0.015 0.039 0.382 0.703
## 30 -0.50 0.017 0.038 0.455 0.649
## 31 -0.45 0.020 0.038 0.531 0.596
## 32 -0.40 0.023 0.037 0.607 0.544
## 33 -0.35 0.025 0.037 0.685 0.493
## 34 -0.30 0.028 0.036 0.764 0.445
## 35 -0.25 0.030 0.036 0.844 0.399
## 36 -0.20 0.033 0.036 0.924 0.355
## 37 -0.15 0.036 0.036 1.005 0.315
## 38 -0.10 0.038 0.035 1.085 0.278
## 39 -0.05 0.041 0.035 1.166 0.244
## 40 0.00 0.044 0.035 1.245 0.213
## 41 0.05 0.046 0.035 1.323 0.186
## 42 0.10 0.049 0.035 1.400 0.161
## 43 0.15 0.051 0.035 1.475 0.140
## 44 0.20 0.054 0.035 1.549 0.121
## 45 0.25 0.057 0.035 1.620 0.105
## 46 0.30 0.059 0.035 1.689 0.091
## 47 0.35 0.062 0.035 1.754 0.079
## 48 0.40 0.064 0.035 1.818 0.069
## 49 0.45 0.067 0.036 1.878 0.060
## 50 0.50 0.070 0.036 1.935 0.053
## 51 0.55 0.072 0.036 1.989 0.047
## 52 0.60 0.075 0.037 2.039 0.041
## 53 0.65 0.078 0.037 2.087 0.037
## 54 0.70 0.080 0.038 2.131 0.033
## 55 0.75 0.083 0.038 2.172 0.030
## 56 0.80 0.085 0.039 2.211 0.027
## 57 0.85 0.088 0.039 2.246 0.025
## 58 0.90 0.091 0.040 2.279 0.023
## 59 0.95 0.093 0.040 2.309 0.021
## 60 1.00 0.096 0.041 2.336 0.019
## 61 1.05 0.098 0.042 2.361 0.018
## 62 1.10 0.101 0.042 2.384 0.017
## 63 1.15 0.104 0.043 2.404 0.016
## 64 1.20 0.106 0.044 2.423 0.015
## 65 1.25 0.109 0.045 2.440 0.015
## 66 1.30 0.112 0.045 2.455 0.014
## 67 1.35 0.114 0.046 2.468 0.014
## 68 1.40 0.117 0.047 2.480 0.013
## 69 1.45 0.119 0.048 2.491 0.013
## 70 1.50 0.122 0.049 2.500 0.012
## 71 1.55 0.125 0.050 2.509 0.012
## 72 1.60 0.127 0.051 2.516 0.012
## 73 1.65 0.130 0.051 2.522 0.012
## 74 1.70 0.132 0.052 2.528 0.011
## 75 1.75 0.135 0.053 2.532 0.011
## 76 1.80 0.138 0.054 2.536 0.011
## 77 1.85 0.140 0.055 2.539 0.011
## 78 1.90 0.143 0.056 2.541 0.011
## 79 1.95 0.146 0.057 2.543 0.011
## 80 2.00 0.148 0.058 2.545 0.011
## 81 2.05 0.151 0.059 2.546 0.011
## 82 2.10 0.153 0.060 2.546 0.011
## 83 2.15 0.156 0.061 2.547 0.011
## 84 2.20 0.159 0.062 2.546 0.011
## 85 2.25 0.161 0.063 2.546 0.011
## 86 2.30 0.164 0.064 2.545 0.011
## 87 2.35 0.166 0.065 2.544 0.011
## 88 2.40 0.169 0.066 2.543 0.011
## 89 2.45 0.172 0.068 2.542 0.011
## 90 2.50 0.174 0.069 2.540 0.011
## 91 2.55 0.177 0.070 2.538 0.011
## 92 2.60 0.179 0.071 2.536 0.011# PRETTIER GRAPHS
fiti <- lm(htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ChildComViolence + ChildNeglectSexual + ViolentVictimization + Destruction_Injury + WitnessViolence*ChildAbuse + q102b_guess_age, data = datamale)
ss <- sim_slopes(fiti, pred = WitnessViolence, modx = ChildAbuse, johnson_neyman = TRUE,control.fdr = TRUE)
ss ## JOHNSON-NEYMAN INTERVAL
##
## When ChildAbuse is INSIDE the interval [1.04, 7.43], the slope of
## WitnessViolence is p < .05.
##
## Note: The range of observed values of ChildAbuse is [-1.95, 2.61]
##
## Interval calculated using false discovery rate adjusted t = 2.39
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of WitnessViolence when ChildAbuse = -0.5069424 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.02 0.04 0.38 0.70
##
## Slope of WitnessViolence when ChildAbuse = 0.4819704 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.07 0.04 1.87 0.06
##
## Slope of WitnessViolence when ChildAbuse = 1.4708832 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.12 0.05 2.56 0.01
probe_interaction(fiti, pred = WitnessViolence, modx = ChildAbuse, cond.int = TRUE, interval = TRUE, jnplot = TRUE)## JOHNSON-NEYMAN INTERVAL
##
## When ChildAbuse is OUTSIDE the interval [-14.71, 0.56], the slope of
## WitnessViolence is p < .05.
##
## Note: The range of observed values of ChildAbuse is [-1.95, 2.61]
## SIMPLE SLOPES ANALYSIS
##
## When ChildAbuse = -0.5069424 (- 1 SD):
##
## Est. S.E. t val. p
## ------------------------------ ------ ------ -------- ------
## Slope of WitnessViolence 0.02 0.04 0.38 0.70
## Conditional intercept 1.95 0.03 67.36 0.00
##
## When ChildAbuse = 0.4819704 (Mean):
##
## Est. S.E. t val. p
## ------------------------------ ------ ------ -------- ------
## Slope of WitnessViolence 0.07 0.04 1.87 0.06
## Conditional intercept 2.09 0.02 118.05 0.00
##
## When ChildAbuse = 1.4708832 (+ 1 SD):
##
## Est. S.E. t val. p
## ------------------------------ ------ ------ -------- ------
## Slope of WitnessViolence 0.12 0.05 2.56 0.01
## Conditional intercept 2.23 0.03 73.61 0.00
d <- interact_plot(fiti, pred = WitnessViolence, modx = ChildAbuse, plot.points = TRUE, modx.values = "terciles", colors = "red", point.size = 1, point.alpha = 0.5, rug = T, jitter = 0.0, x.label = "Witness violence", y.label = "PTSD symptoms", interval = T, legend.main = "Physical &\nemotional abuse", vary.lty = F)## Medians of each tercile of ChildAbuse are -0.396, 0.244, 1.669d+ theme_2 + scale_y_continuous(limits = c(1.5, 2.6)) + scale_x_continuous(n.breaks = 10, limits = c(-1.5, 2.5))
Logistic regression models predicting PTSD diagnosis in males & females
I’ll only use the PTSD-4 scale because it includes all items, not just selected ones. Original HTQ4 total scale has 40 items, and we only asked 33. However, we did ask all that go into PTSD4.
dataf <- dataf %>% mutate(PTSD = case_when(htq_ptsd_dsm >= 2.5 ~ 1,
htq_ptsd_dsm < 2.5 ~ 0,
is.na(htq_ptsd_dsm) ~ NA_real_)) %>% mutate(PTSD = as.ordered(PTSD))
datafemale = dataf %>% filter(respondent_cat == 0) %>% dplyr::rename(Age = q102b_guess_age)
model <- glm(PTSD ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + Age, data = datafemale, family = binomial)
performance::r2(model)## # R2 for Logistic Regression
## Tjur's R2: 0.118## Parameter | Log-Odds | SE | 95% CI | z | p
## -----------------------------------------------------------------------------
## (Intercept) | -0.79 | 0.06 | [-0.90, -0.69] | -14.43 | < .001
## ConflictTrauma | 0.07 | 0.09 | [-0.10, 0.24] | 0.81 | 0.416
## Isolation Loss | 0.20 | 0.06 | [ 0.07, 0.32] | 3.07 | 0.002
## ViolentVictimization | 0.32 | 0.10 | [ 0.12, 0.53] | 3.10 | 0.002
## Destruction Injury | 0.03 | 0.11 | [-0.18, 0.24] | 0.29 | 0.772
## WitnessViolence | 0.10 | 0.09 | [-0.07, 0.26] | 1.15 | 0.252
## ChildAbuse | 0.32 | 0.07 | [ 0.19, 0.45] | 4.76 | < .001
## ChildNeglectSexual | 0.25 | 0.07 | [ 0.12, 0.38] | 3.84 | < .001
## ChildComViolence | 2.28e-03 | 0.06 | [-0.12, 0.12] | 0.04 | 0.970
## Age | 0.04 | 9.84e-03 | [ 0.02, 0.06] | 4.37 | < .001##
## Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
## computed using a Wald z-distribution approximation.##
## The model has a log- or logit-link. Consider using `exponentiate =
## TRUE` to interpret coefficients as ratios.## Parameter | Log-Odds | SE | 95% CI | z | p
## -------------------------------------------------------------------------
## (Intercept) | -1.08 | 0.05 | [-1.19, -0.98] | -20.10 | < .001
## ConflictTrauma | 0.07 | 0.08 | [-0.09, 0.23] | 0.81 | 0.416
## Isolation Loss | 0.19 | 0.06 | [ 0.07, 0.32] | 3.07 | 0.002
## ViolentVictimization | 0.23 | 0.08 | [ 0.09, 0.38] | 3.10 | 0.002
## Destruction Injury | 0.03 | 0.10 | [-0.17, 0.23] | 0.29 | 0.772
## WitnessViolence | 0.09 | 0.08 | [-0.06, 0.24] | 1.15 | 0.252
## ChildAbuse | 0.31 | 0.06 | [ 0.18, 0.44] | 4.76 | < .001
## ChildNeglectSexual | 0.22 | 0.06 | [ 0.11, 0.34] | 3.84 | < .001
## ChildComViolence | 2.26e-03 | 0.06 | [-0.11, 0.12] | 0.04 | 0.970
## Age | 0.22 | 0.05 | [ 0.12, 0.32] | 4.37 | < .001##
## Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
## computed using a Wald z-distribution approximation.plot(parameters(model, standardize = "refit")) +
ggplot2::labs(title = "Log-Odds of childhood and war trauma on a PTSD diagnosis\nin pregnant women")
ggsave("logregfemales.png", width =6, height = 6.2, unit = "in", dpi = 300, bg = 'white')
select_parameters(model, standardize = "refit") ##
## Call: glm(formula = PTSD ~ ConflictTrauma + Isolation_Loss + ViolentVictimization +
## ChildAbuse + ChildNeglectSexual + Age, family = binomial,
## data = datafemale)
##
## Coefficients:
## (Intercept) ConflictTrauma Isolation_Loss
## -0.79540 0.11823 0.21461
## ViolentVictimization ChildAbuse ChildNeglectSexual
## 0.39543 0.32030 0.24721
## Age
## 0.04269
##
## Degrees of Freedom: 2123 Total (i.e. Null); 2117 Residual
## (199 observations deleted due to missingness)
## Null Deviance: 2508
## Residual Deviance: 2262 AIC: 2276## [1] 1.239378## [1] 1.485023## [1] 1.377541## [1] 1.280448## We fitted a logistic model (estimated using ML) to predict PTSD with
## ConflictTrauma, Isolation_Loss, ViolentVictimization, Destruction_Injury,
## WitnessViolence, ChildAbuse, ChildNeglectSexual, ChildComViolence and Age
## (formula: PTSD ~ ConflictTrauma + Isolation_Loss + ViolentVictimization +
## Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual +
## ChildComViolence + Age). The model's explanatory power is weak (Tjur's R2 =
## 0.12). The model's intercept, corresponding to ConflictTrauma = 0,
## Isolation_Loss = 0, ViolentVictimization = 0, Destruction_Injury = 0,
## WitnessViolence = 0, ChildAbuse = 0, ChildNeglectSexual = 0, ChildComViolence =
## 0 and Age = 0, is at -0.79 (95% CI [-0.90, -0.69], p < .001). Within this
## model:
##
## - The effect of ConflictTrauma is statistically non-significant and positive
## (beta = 0.07, 95% CI [-0.10, 0.24], p = 0.416; Std. beta = 0.07, 95% CI [-0.09,
## 0.23])
## - The effect of Isolation Loss is statistically significant and positive (beta
## = 0.20, 95% CI [0.07, 0.32], p = 0.002; Std. beta = 0.19, 95% CI [0.07, 0.32])
## - The effect of ViolentVictimization is statistically significant and positive
## (beta = 0.32, 95% CI [0.12, 0.53], p = 0.002; Std. beta = 0.23, 95% CI [0.09,
## 0.38])
## - The effect of Destruction Injury is statistically non-significant and
## positive (beta = 0.03, 95% CI [-0.18, 0.24], p = 0.772; Std. beta = 0.03, 95%
## CI [-0.17, 0.23])
## - The effect of WitnessViolence is statistically non-significant and positive
## (beta = 0.10, 95% CI [-0.07, 0.26], p = 0.252; Std. beta = 0.09, 95% CI [-0.06,
## 0.24])
## - The effect of ChildAbuse is statistically significant and positive (beta =
## 0.32, 95% CI [0.19, 0.45], p < .001; Std. beta = 0.31, 95% CI [0.18, 0.44])
## - The effect of ChildNeglectSexual is statistically significant and positive
## (beta = 0.25, 95% CI [0.12, 0.38], p < .001; Std. beta = 0.22, 95% CI [0.11,
## 0.34])
## - The effect of ChildComViolence is statistically non-significant and positive
## (beta = 2.28e-03, 95% CI [-0.12, 0.12], p = 0.970; Std. beta = 2.26e-03, 95% CI
## [-0.11, 0.12])
## - The effect of Age is statistically significant and positive (beta = 0.04, 95%
## CI [0.02, 0.06], p < .001; Std. beta = 0.22, 95% CI [0.12, 0.32])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.####
datamale = dataf %>% filter(respondent_cat == 1) %>% dplyr::rename(Age = q102b_guess_age)
model <- glm(PTSD ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + Age, data = datamale, family = binomial)
performance::r2(model)## # R2 for Logistic Regression
## Tjur's R2: 0.303## Parameter | Log-Odds | SE | 95% CI | z | p
## ------------------------------------------------------------------------
## (Intercept) | -1.76 | 0.22 | [-2.21, -1.35] | -8.03 | < .001
## ConflictTrauma | 0.26 | 0.15 | [-0.03, 0.55] | 1.76 | 0.079
## Isolation Loss | -0.21 | 0.12 | [-0.45, 0.02] | -1.75 | 0.080
## ViolentVictimization | 0.74 | 0.17 | [ 0.41, 1.08] | 4.35 | < .001
## Destruction Injury | -0.11 | 0.19 | [-0.48, 0.25] | -0.60 | 0.550
## WitnessViolence | 0.28 | 0.22 | [-0.15, 0.72] | 1.25 | 0.210
## ChildAbuse | 0.56 | 0.14 | [ 0.29, 0.83] | 4.01 | < .001
## ChildNeglectSexual | 0.04 | 0.09 | [-0.15, 0.22] | 0.39 | 0.695
## ChildComViolence | 0.34 | 0.13 | [ 0.08, 0.61] | 2.56 | 0.010
## Age | 0.03 | 0.02 | [-0.01, 0.06] | 1.61 | 0.108##
## Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
## computed using a Wald z-distribution approximation.## Parameter | Log-Odds | SE | 95% CI | z | p
## ------------------------------------------------------------------------
## (Intercept) | -0.39 | 0.10 | [-0.58, -0.19] | -3.85 | < .001
## ConflictTrauma | 0.28 | 0.16 | [-0.03, 0.60] | 1.76 | 0.079
## Isolation Loss | -0.20 | 0.11 | [-0.43, 0.02] | -1.75 | 0.080
## ViolentVictimization | 0.73 | 0.17 | [ 0.40, 1.06] | 4.35 | < .001
## Destruction Injury | -0.10 | 0.17 | [-0.44, 0.23] | -0.60 | 0.550
## WitnessViolence | 0.22 | 0.17 | [-0.12, 0.57] | 1.25 | 0.210
## ChildAbuse | 0.55 | 0.14 | [ 0.28, 0.83] | 4.01 | < .001
## ChildNeglectSexual | 0.04 | 0.11 | [-0.17, 0.25] | 0.39 | 0.695
## ChildComViolence | 0.32 | 0.13 | [ 0.08, 0.57] | 2.56 | 0.010
## Age | 0.17 | 0.10 | [-0.03, 0.37] | 1.61 | 0.108##
## Uncertainty intervals (profile-likelihood) and p-values (two-tailed)
## computed using a Wald z-distribution approximation.plot(parameters(model, standardize = "refit")) +
ggplot2::labs(title = "Log-Odds of childhood and war trauma on a PTSD diagnosis\nin fathers")
ggsave("logregmales.png", width =6, height = 6.2, unit = "in", dpi = 300, bg = 'white')
select_parameters(model, standardize = "refit") ##
## Call: glm(formula = PTSD ~ ConflictTrauma + Isolation_Loss + ViolentVictimization +
## ChildAbuse + ChildComViolence + Age, family = binomial, data = datamale)
##
## Coefficients:
## (Intercept) ConflictTrauma Isolation_Loss
## -1.6817 0.2623 -0.2013
## ViolentVictimization ChildAbuse ChildComViolence
## 0.8320 0.5869 0.3297
## Age
## 0.0240
##
## Degrees of Freedom: 600 Total (i.e. Null); 594 Residual
## (41 observations deleted due to missingness)
## Null Deviance: 821.1
## Residual Deviance: 621 AIC: 635## [1] 2.29791## [1] 1.798405## [1] 1.390551## We fitted a logistic model (estimated using ML) to predict PTSD with
## ConflictTrauma, Isolation_Loss, ViolentVictimization, Destruction_Injury,
## WitnessViolence, ChildAbuse, ChildNeglectSexual, ChildComViolence and Age
## (formula: PTSD ~ ConflictTrauma + Isolation_Loss + ViolentVictimization +
## Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual +
## ChildComViolence + Age). The model's explanatory power is substantial (Tjur's
## R2 = 0.30). The model's intercept, corresponding to ConflictTrauma = 0,
## Isolation_Loss = 0, ViolentVictimization = 0, Destruction_Injury = 0,
## WitnessViolence = 0, ChildAbuse = 0, ChildNeglectSexual = 0, ChildComViolence =
## 0 and Age = 0, is at -1.76 (95% CI [-2.21, -1.35], p < .001). Within this
## model:
##
## - The effect of ConflictTrauma is statistically non-significant and positive
## (beta = 0.26, 95% CI [-0.03, 0.55], p = 0.079; Std. beta = 0.28, 95% CI [-0.03,
## 0.60])
## - The effect of Isolation Loss is statistically non-significant and negative
## (beta = -0.21, 95% CI [-0.45, 0.02], p = 0.080; Std. beta = -0.20, 95% CI
## [-0.43, 0.02])
## - The effect of ViolentVictimization is statistically significant and positive
## (beta = 0.74, 95% CI [0.41, 1.08], p < .001; Std. beta = 0.73, 95% CI [0.40,
## 1.06])
## - The effect of Destruction Injury is statistically non-significant and
## negative (beta = -0.11, 95% CI [-0.48, 0.25], p = 0.550; Std. beta = -0.10, 95%
## CI [-0.44, 0.23])
## - The effect of WitnessViolence is statistically non-significant and positive
## (beta = 0.28, 95% CI [-0.15, 0.72], p = 0.210; Std. beta = 0.22, 95% CI [-0.12,
## 0.57])
## - The effect of ChildAbuse is statistically significant and positive (beta =
## 0.56, 95% CI [0.29, 0.83], p < .001; Std. beta = 0.55, 95% CI [0.28, 0.83])
## - The effect of ChildNeglectSexual is statistically non-significant and
## positive (beta = 0.04, 95% CI [-0.15, 0.22], p = 0.695; Std. beta = 0.04, 95%
## CI [-0.17, 0.25])
## - The effect of ChildComViolence is statistically significant and positive
## (beta = 0.34, 95% CI [0.08, 0.61], p = 0.010; Std. beta = 0.32, 95% CI [0.08,
## 0.57])
## - The effect of Age is statistically non-significant and positive (beta = 0.03,
## 95% CI [-5.57e-03, 0.06], p = 0.108; Std. beta = 0.17, 95% CI [-0.03, 0.37])
##
## Standardized parameters were obtained by fitting the model on a standardized
## version of the dataset. 95% Confidence Intervals (CIs) and p-values were
## computed using a Wald z-distribution approximation.## Variable | Level | n_Obs | percentage_Obs
## -------------------------------------------
## PTSD | 0 | 1572 | 67.67
## PTSD | 1 | 615 | 26.47
## PTSD | missing | 136 | 5.85## Variable | Level | n_Obs | percentage_Obs
## -------------------------------------------
## PTSD | 0 | 357 | 55.61
## PTSD | 1 | 265 | 41.28
## PTSD | missing | 20 | 3.12model_obs <- "
htq_ptsd_total ~ ConflictTrauma + Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence + q102b_guess_age
ConflictTrauma ~~ Isolation_Loss + ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence
Isolation_Loss ~~ ViolentVictimization + Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual + ChildComViolence
ViolentVictimization ~~ Destruction_Injury + WitnessViolence + ChildAbuse + ChildNeglectSexual
ViolentVictimization~~0*ChildComViolence
Destruction_Injury ~~ WitnessViolence + ChildAbuse + ChildComViolence
WitnessViolence ~~ ChildAbuse + ChildNeglectSexual + ChildComViolence
ChildAbuse ~~ 0*ChildNeglectSexual
ChildAbuse ~~ ChildComViolence
ChildNeglectSexual ~~ 0*ChildComViolence
ChildNeglectSexual ~~ 0*Destruction_Injury
ConflictTrauma ~ 0*1
Isolation_Loss ~ 0*1
ViolentVictimization ~ 0*1
Destruction_Injury ~ 0*1
WitnessViolence ~ 0*1
ChildAbuse ~0*1
ChildNeglectSexual ~0*1
ChildComViolence ~0*1
"
fit_obs <- sem(model_obs, data = dataf,estimator = "ML", missing = "FIML.x", meanstructure = T, fixed.x = F)
summary(fit_obs, fit.measures = TRUE, standardized = TRUE, rsquare = T)
model_obs <- "
htq_ptsd_total ~ htq1_sum + ace_frequency
htq1_sum ~~ ace_frequency
"
fit_obs <- sem(model_obs, data = dataf,estimator = "ML", missing = "FIML.x", meanstructure = T, fixed.x = F)
summary(fit_obs, fit.measures = TRUE, standardized = TRUE, rsquare = T)